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Key Question 3 Meta-analysis.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## KQ3: Pritts Meta-analysis Update\n",
"\n",
"In key question 3, we address the risk of sarcoma dissemination following morcellation of fibroids. We identified a recently published analysis conducted by Elizabeth Pritts and her colleagues (2015) to estimate the prevalence of occult leiomyosarcoma at time of treatment for presumed benign tumors (fibroids). We updated their search and used similar eligibility criteria to identify papers published since 2014. We extracted from these papers the number of women who were treated for uterine fibroids and the cases of leiomyosarcoma subsequently identified. We have combined these data with the data from the 134 publications that Pritts et al included in their analysis for a total of LMS rates from 148 sources."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pymc3 as pm\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import pdb\n",
"sns.set()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Author</th>\n",
" <th>Year</th>\n",
" <th>Design</th>\n",
" <th>Procedure</th>\n",
" <th>Indication</th>\n",
" <th>Age, Mean</th>\n",
" <th>Age, SD</th>\n",
" <th>Age, Median</th>\n",
" <th>Age, Min</th>\n",
" <th>Age, Max</th>\n",
" <th>Age, Other</th>\n",
" <th>LMS</th>\n",
" <th>Population</th>\n",
" <th>Tumors</th>\n",
" <th>InPritts</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Line</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>Adelusola KA, Ogunniyi SO</td>\n",
" <td>2001</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19.0</td>\n",
" <td>89</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>177.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>Ahmed AA, Stachurski J, Abdel Aziz E et al</td>\n",
" <td>2002</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.0</td>\n",
" <td>65</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>Angle HS, Cohen SM, Hidlebaugh D</td>\n",
" <td>1995</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>41.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24.0</td>\n",
" <td>81</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>41.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>Banaczek Z, Sikora K, Lewandowska-Andruszuk I</td>\n",
" <td>2004</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>44.5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.0</td>\n",
" <td>73</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>309.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>Barbieri R, Dilena M, Chumas J, Rein MS, Fried...</td>\n",
" <td>1993</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>33.7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>36.3</td>\n",
" <td>0.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Author Year Design \\\n",
"Line \n",
"1.0 Adelusola KA, Ogunniyi SO 2001 Retrospective \n",
"2.0 Ahmed AA, Stachurski J, Abdel Aziz E et al 2002 Prospective \n",
"3.0 Angle HS, Cohen SM, Hidlebaugh D 1995 Retrospective \n",
"4.0 Banaczek Z, Sikora K, Lewandowska-Andruszuk I 2004 Retrospective \n",
"5.0 Barbieri R, Dilena M, Chumas J, Rein MS, Fried... 1993 RCT \n",
"\n",
" Procedure Indication Age, Mean Age, SD Age, Median Age, Min Age, Max \\\n",
"Line \n",
"1.0 NE NE NaN NaN NaN 19.0 89 \n",
"2.0 NE NE NaN NaN NaN 29.0 65 \n",
"3.0 NE NE 41.0 NaN NaN 24.0 81 \n",
"4.0 NE NE 44.5 NaN NaN 29.0 73 \n",
"5.0 NE NE 33.7 NaN NaN NaN NaN \n",
"\n",
" Age, Other LMS Population Tumors InPritts \n",
"Line \n",
"1.0 NaN 0.0 177.0 0.0 1.0 \n",
"2.0 NaN 0.0 10.0 0.0 1.0 \n",
"3.0 NaN 0.0 41.0 0.0 1.0 \n",
"4.0 NaN 0.0 309.0 0.0 1.0 \n",
"5.0 36.3 0.0 20.0 0.0 1.0 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kq3_data = pd.read_excel('data/UF CER KQ3 Data for Analysis.xlsx', sheetname='5.UFKQ3Data', \n",
" na_values=['NR', 'NA'],\n",
" index_col=0)[['Author', 'Year', 'Design', 'Procedure', 'Indication',\n",
" 'Age, Mean', 'Age, SD', 'Age, Median', 'Age, Min', 'Age, Max', 'Age, Other',\n",
" 'LMS','Population','Tumors','InPritts']].dropna(thresh=7)\n",
"kq3_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Missing values"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Author 0\n",
"Year 0\n",
"Design 0\n",
"Procedure 0\n",
"Indication 2\n",
"Age, Mean 67\n",
"Age, SD 138\n",
"Age, Median 142\n",
"Age, Min 58\n",
"Age, Max 59\n",
"Age, Other 114\n",
"LMS 0\n",
"Population 0\n",
"Tumors 0\n",
"InPritts 0\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kq3_data.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"kq3_data['age_max'] = kq3_data['Age, Max'].replace('50+', 50)\n",
"kq3_data = kq3_data.rename(columns={'Age, Min': 'age_min',\n",
" 'Age, Mean': 'age_mean',\n",
" 'Age, SD': 'age_sd',\n",
" 'Age, Median': 'age_med'})"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Author</th>\n",
" <th>Year</th>\n",
" <th>Design</th>\n",
" <th>Procedure</th>\n",
" <th>Indication</th>\n",
" <th>age_mean</th>\n",
" <th>age_sd</th>\n",
" <th>age_med</th>\n",
" <th>age_min</th>\n",
" <th>Age, Max</th>\n",
" <th>Age, Other</th>\n",
" <th>LMS</th>\n",
" <th>Population</th>\n",
" <th>Tumors</th>\n",
" <th>InPritts</th>\n",
" <th>age_max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Line</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>Barbieri R, Dilena M, Chumas J, Rein MS, Fried...</td>\n",
" <td>1993</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>33.70</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>36.3</td>\n",
" <td>0.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <td>Begum S, Khan S</td>\n",
" <td>2004</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>91.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8.0</th>\n",
" <td>Betjes HE, Hanstede M, Emanuel M, Stewart EA</td>\n",
" <td>2009</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>44.30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>44.6, 44</td>\n",
" <td>0.0</td>\n",
" <td>539.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10.0</th>\n",
" <td>Bronz L, Suter T, Rusca T</td>\n",
" <td>1997</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11.0</th>\n",
" <td>Bushaqer NJ, Dayoub N</td>\n",
" <td>2014</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>36.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>137.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12.0</th>\n",
" <td>Butt JL, Jeffery ST, Van der Spuy ZM</td>\n",
" <td>2012</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>106.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16.0</th>\n",
" <td>Colgan TJ, Pendergast S, LeBlanc M</td>\n",
" <td>1993</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>36.90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>41.4</td>\n",
" <td>0.0</td>\n",
" <td>77.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19.0</th>\n",
" <td>De Falco M, Staibano S, Mascolo M, Mignona C, ...</td>\n",
" <td>2009</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>37.30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>37.5</td>\n",
" <td>0.0</td>\n",
" <td>62.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22.0</th>\n",
" <td>DiLieto A, De Falco M, Mansueto G, De Rosa G, ...</td>\n",
" <td>2005</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>36.80</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>37.2</td>\n",
" <td>0.0</td>\n",
" <td>70.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32.0</th>\n",
" <td>Gavai M, Hupuczi P, Papp Z</td>\n",
" <td>2006</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>33.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>504.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44.0</th>\n",
" <td>Hoffman M, DeCesare S, Kalter C</td>\n",
" <td>1994</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>41.90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>42.7</td>\n",
" <td>0.0</td>\n",
" <td>47.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45.0</th>\n",
" <td>Huang JQ, Lathi RB, Lemyre M, Rodriguez HE, Ne...</td>\n",
" <td>2010</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>41.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>45</td>\n",
" <td>0.0</td>\n",
" <td>131.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46.0</th>\n",
" <td>Jansen FW, de Kroon CD, van Dongen H, Grooters...</td>\n",
" <td>2006</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>43.80</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>67.4</td>\n",
" <td>0.0</td>\n",
" <td>89.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48.0</th>\n",
" <td>Johns D, Diamond MP</td>\n",
" <td>1994</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>39.20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>55.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49.0</th>\n",
" <td>Kafy S, Huang JYJ, Al-Sunaidi M, Wiener D, Tul...</td>\n",
" <td>2006</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>59.60</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>46.8, 47.0</td>\n",
" <td>0.0</td>\n",
" <td>934.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50.0</th>\n",
" <td>Kalogiannidis I, Prapas N, Xiromeritis P et al</td>\n",
" <td>2010</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>34.80</td>\n",
" <td>4.50</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>37.7 ± 4.8</td>\n",
" <td>0.0</td>\n",
" <td>75.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51.0</th>\n",
" <td>Kamikabeya TS, Etchebehere RM, Nomelini RS, Mu...</td>\n",
" <td>2010</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1364.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52.0</th>\n",
" <td>Kiltz RJ, Rutgers J, Phillips J, Murugesapilla...</td>\n",
" <td>1994</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>31.00</td>\n",
" <td>1.80</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>28 ± 1.3, 30 ± 2.1</td>\n",
" <td>0.0</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60.0</th>\n",
" <td>Levens ED, Wesley R, Prekumar A, Blocker W, Ni...</td>\n",
" <td>2009</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61.0</th>\n",
" <td>Lim SS, Sockalingam JK, Tan PC</td>\n",
" <td>2008</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>46.50</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>66.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62.0</th>\n",
" <td>Litta P, Fantinato S, Calonaci F, Cosmi E, Fil...</td>\n",
" <td>2010</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>37.34</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>38.11</td>\n",
" <td>0.0</td>\n",
" <td>160.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66.0</th>\n",
" <td>MacKenzie IZ, Naish C, Rees M, Manek S</td>\n",
" <td>2004</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>47.50</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>118.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68.0</th>\n",
" <td>Mansour FW, Kives S, Urbach DR, Lefebvre G</td>\n",
" <td>2012</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>34.70</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>35.3</td>\n",
" <td>0.0</td>\n",
" <td>59.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73.0</th>\n",
" <td>Milad MP, Morrison K, Sokol A, Miller D, Kirkp...</td>\n",
" <td>2001</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>43.90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>47.3</td>\n",
" <td>0.0</td>\n",
" <td>69.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80.0</th>\n",
" <td>Obed JY, Bako B, Usman J, Moruppa JY, Kadas S</td>\n",
" <td>2011</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>30.10</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>331.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84.0</th>\n",
" <td>Palomba S, Orio F Jr, Russo T, Falbo A, Tolino...</td>\n",
" <td>2005</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>53.40</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.2</td>\n",
" <td>0.0</td>\n",
" <td>40.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85.0</th>\n",
" <td>Palomba S, Zupi E, Falbo A, Russo T, Marconi D...</td>\n",
" <td>2010</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>30.20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>28.9</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92.0</th>\n",
" <td>Radosa MP, Owsianowski Z, Mothes A, Weisheit J...</td>\n",
" <td>2014</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>37.90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>221.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93.0</th>\n",
" <td>Rein MS, Friedman AJ, Stuart JM, MacLaughlin DT</td>\n",
" <td>1990</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>\"premenopausal\"</td>\n",
" <td>0.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94.0</th>\n",
" <td>Reiter RC, Wagner PL, Gambone JC</td>\n",
" <td>1992</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>41.50</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>42.9</td>\n",
" <td>0.0</td>\n",
" <td>104.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95.0</th>\n",
" <td>Rosenblatt P, Makai G, DiSciullo A</td>\n",
" <td>2010</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>50.20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>24.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96.0</th>\n",
" <td>Rovio PH, Helin R, Heinonen PK</td>\n",
" <td>2009</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>44.70</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>53.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97.0</th>\n",
" <td>Rutgers JL, Spong CY, Sinow R, Heiner J</td>\n",
" <td>1995</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>38.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>39</td>\n",
" <td>0.0</td>\n",
" <td>22.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99.0</th>\n",
" <td>Samaila M, Adesiyun AG, Agunbiade OS, Mohammed...</td>\n",
" <td>2009</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>44.60</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>196.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100.0</th>\n",
" <td>Sayyah-Melli M, Tehrani-Gadim S, Dastranj-Tabr...</td>\n",
" <td>2009</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>39.67</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>36.87</td>\n",
" <td>0.0</td>\n",
" <td>23.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101.0</th>\n",
" <td>Schutz K, Possover M, Merker A, Michels W, Sch...</td>\n",
" <td>2002</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>47.5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>48, 49</td>\n",
" <td>0.0</td>\n",
" <td>48.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104.0</th>\n",
" <td>Seracchioli R, Venturoli S, Vianello F, Govoni...</td>\n",
" <td>2002</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>46.30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>47.4</td>\n",
" <td>0.0</td>\n",
" <td>122.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105.0</th>\n",
" <td>Shen C, Wu M, Kung F, Huang FJ, Hsieh CH, Lan ...</td>\n",
" <td>2003</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>45.50</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>1521.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108.0</th>\n",
" <td>Silva BAC, Falcone T, Bradley L, Goldberg J, M...</td>\n",
" <td>2000</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>37.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>37.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109.0</th>\n",
" <td>Silva BAC, Falcone T, Bradley L, Goldberg J, M...</td>\n",
" <td>2000</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>37.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>37.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110.0</th>\n",
" <td>Sinha R, Hegde A, Mahajan C, Dubey N, Sundaram M</td>\n",
" <td>2008</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>34.44</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>33.98</td>\n",
" <td>2.0</td>\n",
" <td>505.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113.0</th>\n",
" <td>Tan J, Sun Y, Zhong B, Dai H, Wang D</td>\n",
" <td>2009</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>36.30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>35.8</td>\n",
" <td>0.0</td>\n",
" <td>80.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118.0</th>\n",
" <td>Van Dongen H, Emanuel MH, Wolterbeek R, Trimbo...</td>\n",
" <td>2008</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>48.20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>49</td>\n",
" <td>0.0</td>\n",
" <td>22.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121.0</th>\n",
" <td>Walid MS, Heaton RL</td>\n",
" <td>2010</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>\"all reproductive age\"</td>\n",
" <td>0.0</td>\n",
" <td>41.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123.0</th>\n",
" <td>Wang CJ, Soong YK, Lee CL</td>\n",
" <td>2007</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>38.5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126.0</th>\n",
" <td>Williams A, Critchley H, Osei J, Ingamells S, ...</td>\n",
" <td>2007</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>\"premenopausal\"</td>\n",
" <td>0.0</td>\n",
" <td>33.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127.0</th>\n",
" <td>Williams CD, Marshburn PB</td>\n",
" <td>1998</td>\n",
" <td>Prospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>38.50</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130.0</th>\n",
" <td>Ylikorkala O, Tiitinen A, Hulkko S, Kivinen S,...</td>\n",
" <td>1995</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>43.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>44.5</td>\n",
" <td>0.0</td>\n",
" <td>101.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132.0</th>\n",
" <td>Yoon HJ, Kyung MS, Jung US, Choi JS</td>\n",
" <td>2007</td>\n",
" <td>Retrospective</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>34.90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>51.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133.0</th>\n",
" <td>Zhu L, Lang JH, Liu CY, SHI HH, Zun ZJ, Fan R</td>\n",
" <td>2009</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>101.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134.0</th>\n",
" <td>Zullo F, Palomba S, Corea D, Pellicano M, Russ...</td>\n",
" <td>2004</td>\n",
" <td>RCT</td>\n",
" <td>NE</td>\n",
" <td>NE</td>\n",
" <td>28.20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>27.1</td>\n",
" <td>0.0</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136.0</th>\n",
" <td>Balgobin S, P. A. Maldonado, K. Chin, J. I. Sc...</td>\n",
" <td>2016</td>\n",
" <td>Retrospective</td>\n",
" <td>hysterectomy</td>\n",
" <td>AUB, leiomyoma, pain, prolapse, stress urinary...</td>\n",
" <td>46.00</td>\n",
" <td>11.30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>435.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138.0</th>\n",
" <td>Raine-Bennett T, L. Y. Tucker, E. Zaritsky, R....</td>\n",
" <td>2016</td>\n",
" <td>Pop based cohort</td>\n",
" <td>hysterectomy</td>\n",
" <td>leiomyoma</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>18.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>172.0</td>\n",
" <td>34603.0</td>\n",
" <td>172.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139.0</th>\n",
" <td>Zhao WC, F. F. Bi, D. Li and Q. Yang</td>\n",
" <td>2015</td>\n",
" <td>Retrospective</td>\n",
" <td>hysterectomy; myomectomy</td>\n",
" <td>uterine fibroids</td>\n",
" <td>48.20</td>\n",
" <td>7.64</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13.0</td>\n",
" <td>10248.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142.0</th>\n",
" <td>Tan-Kim J, K. A. Hartzell, C. S. Reinsch, C. H...</td>\n",
" <td>2015</td>\n",
" <td>Retrospective</td>\n",
" <td>hysterectomy</td>\n",
" <td>NaN</td>\n",
" <td>46.00</td>\n",
" <td>6.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>941.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144.0</th>\n",
" <td>Cormio G, Loizzi, Ceci O, Leone L, Selvaggi L ...</td>\n",
" <td>2015</td>\n",
" <td>Retrospective</td>\n",
" <td>myomectomy</td>\n",
" <td>bleeding due to uterine fibroid</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>588.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145.0</th>\n",
" <td>Zhang J, J. Zhang, Y. Dai, L. Zhu, J. Lang and...</td>\n",
" <td>2015</td>\n",
" <td>Retrospective</td>\n",
" <td>myomectomy</td>\n",
" <td>menstrual disturbances, pelvic pain, myoma det...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>4248.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147.0</th>\n",
" <td>Bojahr B, De Wilde RL, and Tchartchian G</td>\n",
" <td>2015</td>\n",
" <td>Retrospective</td>\n",
" <td>hysterectomy</td>\n",
" <td>symptomatic uterine myomas</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>10731.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148.0</th>\n",
" <td>Lieng M, E. Berner and B. Busund</td>\n",
" <td>2015</td>\n",
" <td>Retrospective</td>\n",
" <td>hysterectomy, myomectomy</td>\n",
" <td>uterine fibroids</td>\n",
" <td>61.20</td>\n",
" <td>12.30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>4771.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Author Year \\\n",
"Line \n",
"5.0 Barbieri R, Dilena M, Chumas J, Rein MS, Fried... 1993 \n",
"6.0 Begum S, Khan S 2004 \n",
"8.0 Betjes HE, Hanstede M, Emanuel M, Stewart EA 2009 \n",
"10.0 Bronz L, Suter T, Rusca T 1997 \n",
"11.0 Bushaqer NJ, Dayoub N 2014 \n",
"12.0 Butt JL, Jeffery ST, Van der Spuy ZM 2012 \n",
"16.0 Colgan TJ, Pendergast S, LeBlanc M 1993 \n",
"19.0 De Falco M, Staibano S, Mascolo M, Mignona C, ... 2009 \n",
"22.0 DiLieto A, De Falco M, Mansueto G, De Rosa G, ... 2005 \n",
"32.0 Gavai M, Hupuczi P, Papp Z 2006 \n",
"44.0 Hoffman M, DeCesare S, Kalter C 1994 \n",
"45.0 Huang JQ, Lathi RB, Lemyre M, Rodriguez HE, Ne... 2010 \n",
"46.0 Jansen FW, de Kroon CD, van Dongen H, Grooters... 2006 \n",
"48.0 Johns D, Diamond MP 1994 \n",
"49.0 Kafy S, Huang JYJ, Al-Sunaidi M, Wiener D, Tul... 2006 \n",
"50.0 Kalogiannidis I, Prapas N, Xiromeritis P et al 2010 \n",
"51.0 Kamikabeya TS, Etchebehere RM, Nomelini RS, Mu... 2010 \n",
"52.0 Kiltz RJ, Rutgers J, Phillips J, Murugesapilla... 1994 \n",
"60.0 Levens ED, Wesley R, Prekumar A, Blocker W, Ni... 2009 \n",
"61.0 Lim SS, Sockalingam JK, Tan PC 2008 \n",
"62.0 Litta P, Fantinato S, Calonaci F, Cosmi E, Fil... 2010 \n",
"66.0 MacKenzie IZ, Naish C, Rees M, Manek S 2004 \n",
"68.0 Mansour FW, Kives S, Urbach DR, Lefebvre G 2012 \n",
"73.0 Milad MP, Morrison K, Sokol A, Miller D, Kirkp... 2001 \n",
"80.0 Obed JY, Bako B, Usman J, Moruppa JY, Kadas S 2011 \n",
"84.0 Palomba S, Orio F Jr, Russo T, Falbo A, Tolino... 2005 \n",
"85.0 Palomba S, Zupi E, Falbo A, Russo T, Marconi D... 2010 \n",
"92.0 Radosa MP, Owsianowski Z, Mothes A, Weisheit J... 2014 \n",
"93.0 Rein MS, Friedman AJ, Stuart JM, MacLaughlin DT 1990 \n",
"94.0 Reiter RC, Wagner PL, Gambone JC 1992 \n",
"95.0 Rosenblatt P, Makai G, DiSciullo A 2010 \n",
"96.0 Rovio PH, Helin R, Heinonen PK 2009 \n",
"97.0 Rutgers JL, Spong CY, Sinow R, Heiner J 1995 \n",
"99.0 Samaila M, Adesiyun AG, Agunbiade OS, Mohammed... 2009 \n",
"100.0 Sayyah-Melli M, Tehrani-Gadim S, Dastranj-Tabr... 2009 \n",
"101.0 Schutz K, Possover M, Merker A, Michels W, Sch... 2002 \n",
"104.0 Seracchioli R, Venturoli S, Vianello F, Govoni... 2002 \n",
"105.0 Shen C, Wu M, Kung F, Huang FJ, Hsieh CH, Lan ... 2003 \n",
"108.0 Silva BAC, Falcone T, Bradley L, Goldberg J, M... 2000 \n",
"109.0 Silva BAC, Falcone T, Bradley L, Goldberg J, M... 2000 \n",
"110.0 Sinha R, Hegde A, Mahajan C, Dubey N, Sundaram M 2008 \n",
"113.0 Tan J, Sun Y, Zhong B, Dai H, Wang D 2009 \n",
"118.0 Van Dongen H, Emanuel MH, Wolterbeek R, Trimbo... 2008 \n",
"121.0 Walid MS, Heaton RL 2010 \n",
"123.0 Wang CJ, Soong YK, Lee CL 2007 \n",
"126.0 Williams A, Critchley H, Osei J, Ingamells S, ... 2007 \n",
"127.0 Williams CD, Marshburn PB 1998 \n",
"130.0 Ylikorkala O, Tiitinen A, Hulkko S, Kivinen S,... 1995 \n",
"132.0 Yoon HJ, Kyung MS, Jung US, Choi JS 2007 \n",
"133.0 Zhu L, Lang JH, Liu CY, SHI HH, Zun ZJ, Fan R 2009 \n",
"134.0 Zullo F, Palomba S, Corea D, Pellicano M, Russ... 2004 \n",
"136.0 Balgobin S, P. A. Maldonado, K. Chin, J. I. Sc... 2016 \n",
"138.0 Raine-Bennett T, L. Y. Tucker, E. Zaritsky, R.... 2016 \n",
"139.0 Zhao WC, F. F. Bi, D. Li and Q. Yang 2015 \n",
"142.0 Tan-Kim J, K. A. Hartzell, C. S. Reinsch, C. H... 2015 \n",
"144.0 Cormio G, Loizzi, Ceci O, Leone L, Selvaggi L ... 2015 \n",
"145.0 Zhang J, J. Zhang, Y. Dai, L. Zhu, J. Lang and... 2015 \n",
"147.0 Bojahr B, De Wilde RL, and Tchartchian G 2015 \n",
"148.0 Lieng M, E. Berner and B. Busund 2015 \n",
"\n",
" Design Procedure \\\n",
"Line \n",
"5.0 RCT NE \n",
"6.0 Prospective NE \n",
"8.0 Retrospective NE \n",
"10.0 Prospective NE \n",
"11.0 Retrospective NE \n",
"12.0 Retrospective NE \n",
"16.0 Retrospective NE \n",
"19.0 RCT NE \n",
"22.0 RCT NE \n",
"32.0 Retrospective NE \n",
"44.0 Prospective NE \n",
"45.0 Retrospective NE \n",
"46.0 Prospective NE \n",
"48.0 Retrospective NE \n",
"49.0 Retrospective NE \n",
"50.0 Prospective NE \n",
"51.0 Retrospective NE \n",
"52.0 Prospective NE \n",
"60.0 RCT NE \n",
"61.0 RCT NE \n",
"62.0 RCT NE \n",
"66.0 Retrospective NE \n",
"68.0 Retrospective NE \n",
"73.0 Prospective NE \n",
"80.0 Prospective NE \n",
"84.0 RCT NE \n",
"85.0 Prospective NE \n",
"92.0 Retrospective NE \n",
"93.0 RCT NE \n",
"94.0 Retrospective NE \n",
"95.0 Retrospective NE \n",
"96.0 Retrospective NE \n",
"97.0 RCT NE \n",
"99.0 Retrospective NE \n",
"100.0 RCT NE \n",
"101.0 RCT NE \n",
"104.0 RCT NE \n",
"105.0 Retrospective NE \n",
"108.0 Prospective NE \n",
"109.0 Retrospective NE \n",
"110.0 Prospective NE \n",
"113.0 RCT NE \n",
"118.0 RCT NE \n",
"121.0 Retrospective NE \n",
"123.0 Prospective NE \n",
"126.0 RCT NE \n",
"127.0 Prospective NE \n",
"130.0 RCT NE \n",
"132.0 Retrospective NE \n",
"133.0 RCT NE \n",
"134.0 RCT NE \n",
"136.0 Retrospective hysterectomy \n",
"138.0 Pop based cohort hysterectomy \n",
"139.0 Retrospective hysterectomy; myomectomy \n",
"142.0 Retrospective hysterectomy \n",
"144.0 Retrospective myomectomy \n",
"145.0 Retrospective myomectomy \n",
"147.0 Retrospective hysterectomy \n",
"148.0 Retrospective hysterectomy, myomectomy \n",
"\n",
" Indication age_mean age_sd \\\n",
"Line \n",
"5.0 NE 33.70 NaN \n",
"6.0 NE NaN NaN \n",
"8.0 NE 44.30 NaN \n",
"10.0 NE NaN NaN \n",
"11.0 NE 36.00 NaN \n",
"12.0 NE NaN NaN \n",
"16.0 NE 36.90 NaN \n",
"19.0 NE 37.30 NaN \n",
"22.0 NE 36.80 NaN \n",
"32.0 NE 33.00 NaN \n",
"44.0 NE 41.90 NaN \n",
"45.0 NE 41.00 NaN \n",
"46.0 NE 43.80 NaN \n",
"48.0 NE 39.20 NaN \n",
"49.0 NE 59.60 NaN \n",
"50.0 NE 34.80 4.50 \n",
"51.0 NE NaN NaN \n",
"52.0 NE 31.00 1.80 \n",
"60.0 NE NaN NaN \n",
"61.0 NE 46.50 NaN \n",
"62.0 NE 37.34 NaN \n",
"66.0 NE 47.50 NaN \n",
"68.0 NE 34.70 NaN \n",
"73.0 NE 43.90 NaN \n",
"80.0 NE 30.10 NaN \n",
"84.0 NE 53.40 NaN \n",
"85.0 NE 30.20 NaN \n",
"92.0 NE 37.90 NaN \n",
"93.0 NE NaN NaN \n",
"94.0 NE 41.50 NaN \n",
"95.0 NE 50.20 NaN \n",
"96.0 NE 44.70 NaN \n",
"97.0 NE 38.00 NaN \n",
"99.0 NE 44.60 NaN \n",
"100.0 NE 39.67 NaN \n",
"101.0 NE NaN NaN \n",
"104.0 NE 46.30 NaN \n",
"105.0 NE 45.50 NaN \n",
"108.0 NE 37.00 NaN \n",
"109.0 NE 37.00 NaN \n",
"110.0 NE 34.44 NaN \n",
"113.0 NE 36.30 NaN \n",
"118.0 NE 48.20 NaN \n",
"121.0 NE NaN NaN \n",
"123.0 NE NaN NaN \n",
"126.0 NE NaN NaN \n",
"127.0 NE 38.50 NaN \n",
"130.0 NE 43.00 NaN \n",
"132.0 NE 34.90 NaN \n",
"133.0 NE NaN NaN \n",
"134.0 NE 28.20 NaN \n",
"136.0 AUB, leiomyoma, pain, prolapse, stress urinary... 46.00 11.30 \n",
"138.0 leiomyoma NaN NaN \n",
"139.0 uterine fibroids 48.20 7.64 \n",
"142.0 NaN 46.00 6.00 \n",
"144.0 bleeding due to uterine fibroid NaN NaN \n",
"145.0 menstrual disturbances, pelvic pain, myoma det... NaN NaN \n",
"147.0 symptomatic uterine myomas NaN NaN \n",
"148.0 uterine fibroids 61.20 12.30 \n",
"\n",
" age_med age_min Age, Max Age, Other LMS Population \\\n",
"Line \n",
"5.0 NaN NaN NaN 36.3 0.0 20.0 \n",
"6.0 NaN NaN NaN NaN 0.0 91.0 \n",
"8.0 NaN NaN NaN 44.6, 44 0.0 539.0 \n",
"10.0 NaN NaN NaN NaN 0.0 25.0 \n",
"11.0 NaN NaN NaN NaN 0.0 137.0 \n",
"12.0 NaN NaN NaN NaN 0.0 106.0 \n",
"16.0 NaN NaN NaN 41.4 0.0 77.0 \n",
"19.0 NaN NaN NaN 37.5 0.0 62.0 \n",
"22.0 NaN NaN NaN 37.2 0.0 70.0 \n",
"32.0 NaN NaN NaN NaN 0.0 504.0 \n",
"44.0 NaN NaN NaN 42.7 0.0 47.0 \n",
"45.0 NaN NaN NaN 45 0.0 131.0 \n",
"46.0 NaN NaN NaN 67.4 0.0 89.0 \n",
"48.0 NaN NaN NaN NaN 0.0 55.0 \n",
"49.0 NaN NaN NaN 46.8, 47.0 0.0 934.0 \n",
"50.0 NaN NaN NaN 37.7 ± 4.8 0.0 75.0 \n",
"51.0 NaN NaN NaN NaN 1.0 1364.0 \n",
"52.0 NaN NaN NaN 28 ± 1.3, 30 ± 2.1 0.0 28.0 \n",
"60.0 NaN NaN NaN NaN 0.0 18.0 \n",
"61.0 NaN NaN NaN NaN 0.0 66.0 \n",
"62.0 NaN NaN NaN 38.11 0.0 160.0 \n",
"66.0 NaN NaN NaN NaN 0.0 118.0 \n",
"68.0 NaN NaN NaN 35.3 0.0 59.0 \n",
"73.0 NaN NaN NaN 47.3 0.0 69.0 \n",
"80.0 NaN NaN NaN NaN 0.0 331.0 \n",
"84.0 NaN NaN NaN 52.2 0.0 40.0 \n",
"85.0 NaN NaN NaN 28.9 0.0 30.0 \n",
"92.0 NaN NaN NaN NaN 0.0 221.0 \n",
"93.0 NaN NaN NaN \"premenopausal\" 0.0 20.0 \n",
"94.0 NaN NaN NaN 42.9 0.0 104.0 \n",
"95.0 NaN NaN NaN NaN 0.0 24.0 \n",
"96.0 NaN NaN NaN NaN 0.0 53.0 \n",
"97.0 NaN NaN NaN 39 0.0 22.0 \n",
"99.0 NaN NaN NaN NaN 0.0 196.0 \n",
"100.0 NaN NaN NaN 36.87 0.0 23.0 \n",
"101.0 47.5 NaN NaN 48, 49 0.0 48.0 \n",
"104.0 NaN NaN NaN 47.4 0.0 122.0 \n",
"105.0 NaN NaN NaN NaN 0.0 1521.0 \n",
"108.0 NaN NaN NaN NaN 0.0 37.0 \n",
"109.0 NaN NaN NaN NaN 0.0 37.0 \n",
"110.0 NaN NaN NaN 33.98 2.0 505.0 \n",
"113.0 NaN NaN NaN 35.8 0.0 80.0 \n",
"118.0 NaN NaN NaN 49 0.0 22.0 \n",
"121.0 NaN NaN NaN \"all reproductive age\" 0.0 41.0 \n",
"123.0 38.5 NaN NaN NaN 0.0 18.0 \n",
"126.0 NaN NaN NaN \"premenopausal\" 0.0 33.0 \n",
"127.0 NaN NaN NaN NaN 0.0 5.0 \n",
"130.0 NaN NaN NaN 44.5 0.0 101.0 \n",
"132.0 NaN NaN NaN NaN 0.0 51.0 \n",
"133.0 NaN NaN NaN NaN 0.0 101.0 \n",
"134.0 NaN NaN NaN 27.1 0.0 60.0 \n",
"136.0 NaN NaN NaN NaN 0.0 435.0 \n",
"138.0 NaN 18.0 NaN NaN 172.0 34603.0 \n",
"139.0 NaN NaN NaN NaN 13.0 10248.0 \n",
"142.0 NaN NaN NaN NaN 3.0 941.0 \n",
"144.0 NaN NaN NaN NaN 3.0 588.0 \n",
"145.0 NaN NaN NaN NaN 1.0 4248.0 \n",
"147.0 NaN NaN NaN NaN 2.0 10731.0 \n",
"148.0 NaN NaN NaN NaN 6.0 4771.0 \n",
"\n",
" Tumors InPritts age_max \n",
"Line \n",
"5.0 0.0 1.0 NaN \n",
"6.0 0.0 1.0 NaN \n",
"8.0 0.0 1.0 NaN \n",
"10.0 0.0 1.0 NaN \n",
"11.0 0.0 1.0 NaN \n",
"12.0 0.0 1.0 NaN \n",
"16.0 0.0 1.0 NaN \n",
"19.0 0.0 1.0 NaN \n",
"22.0 0.0 1.0 NaN \n",
"32.0 0.0 1.0 NaN \n",
"44.0 0.0 1.0 NaN \n",
"45.0 0.0 1.0 NaN \n",
"46.0 0.0 1.0 NaN \n",
"48.0 0.0 1.0 NaN \n",
"49.0 0.0 1.0 NaN \n",
"50.0 0.0 1.0 NaN \n",
"51.0 1.0 1.0 NaN \n",
"52.0 0.0 1.0 NaN \n",
"60.0 0.0 1.0 NaN \n",
"61.0 0.0 1.0 NaN \n",
"62.0 0.0 1.0 NaN \n",
"66.0 0.0 1.0 NaN \n",
"68.0 0.0 1.0 NaN \n",
"73.0 0.0 1.0 NaN \n",
"80.0 0.0 1.0 NaN \n",
"84.0 0.0 1.0 NaN \n",
"85.0 0.0 1.0 NaN \n",
"92.0 0.0 1.0 NaN \n",
"93.0 0.0 1.0 NaN \n",
"94.0 0.0 1.0 NaN \n",
"95.0 0.0 1.0 NaN \n",
"96.0 0.0 1.0 NaN \n",
"97.0 0.0 1.0 NaN \n",
"99.0 0.0 1.0 NaN \n",
"100.0 0.0 1.0 NaN \n",
"101.0 0.0 1.0 NaN \n",
"104.0 0.0 1.0 NaN \n",
"105.0 0.0 1.0 NaN \n",
"108.0 0.0 1.0 NaN \n",
"109.0 0.0 1.0 NaN \n",
"110.0 2.0 1.0 NaN \n",
"113.0 0.0 1.0 NaN \n",
"118.0 0.0 1.0 NaN \n",
"121.0 0.0 1.0 NaN \n",
"123.0 0.0 1.0 NaN \n",
"126.0 0.0 1.0 NaN \n",
"127.0 0.0 1.0 NaN \n",
"130.0 0.0 1.0 NaN \n",
"132.0 0.0 1.0 NaN \n",
"133.0 0.0 1.0 NaN \n",
"134.0 0.0 1.0 NaN \n",
"136.0 0.0 0.0 NaN \n",
"138.0 172.0 0.0 NaN \n",
"139.0 13.0 0.0 NaN \n",
"142.0 3.0 0.0 NaN \n",
"144.0 3.0 0.0 NaN \n",
"145.0 1.0 0.0 NaN \n",
"147.0 2.0 0.0 NaN \n",
"148.0 6.0 0.0 NaN "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kq3_data[kq3_data.age_max.isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age_mean</th>\n",
" <th>age_sd</th>\n",
" <th>age_med</th>\n",
" <th>age_min</th>\n",
" <th>LMS</th>\n",
" <th>Population</th>\n",
" <th>Tumors</th>\n",
" <th>InPritts</th>\n",
" <th>age_max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>81.000000</td>\n",
" <td>10.000000</td>\n",
" <td>6.000000</td>\n",
" <td>90.000000</td>\n",
" <td>148.000000</td>\n",
" <td>148.000000</td>\n",
" <td>148.000000</td>\n",
" <td>148.000000</td>\n",
" <td>89.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>41.744815</td>\n",
" <td>7.071000</td>\n",
" <td>39.833333</td>\n",
" <td>26.860000</td>\n",
" <td>1.750000</td>\n",
" <td>790.472973</td>\n",
" <td>1.750000</td>\n",
" <td>0.905405</td>\n",
" <td>61.638202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6.269069</td>\n",
" <td>3.044779</td>\n",
" <td>4.910261</td>\n",
" <td>6.393638</td>\n",
" <td>14.256577</td>\n",
" <td>3299.459447</td>\n",
" <td>14.256577</td>\n",
" <td>0.293648</td>\n",
" <td>13.414371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>28.200000</td>\n",
" <td>1.800000</td>\n",
" <td>35.000000</td>\n",
" <td>18.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>34.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>37.200000</td>\n",
" <td>6.025000</td>\n",
" <td>36.475000</td>\n",
" <td>21.250000</td>\n",
" <td>0.000000</td>\n",
" <td>40.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>51.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>41.900000</td>\n",
" <td>6.600000</td>\n",
" <td>38.200000</td>\n",
" <td>25.000000</td>\n",
" <td>0.000000</td>\n",
" <td>90.500000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>60.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>45.900000</td>\n",
" <td>7.812500</td>\n",
" <td>42.700000</td>\n",
" <td>31.000000</td>\n",
" <td>0.000000</td>\n",
" <td>297.750000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>71.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>61.200000</td>\n",
" <td>12.300000</td>\n",
" <td>47.500000</td>\n",
" <td>44.000000</td>\n",
" <td>172.000000</td>\n",
" <td>34603.000000</td>\n",
" <td>172.000000</td>\n",
" <td>1.000000</td>\n",
" <td>96.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age_mean age_sd age_med age_min LMS Population \\\n",
"count 81.000000 10.000000 6.000000 90.000000 148.000000 148.000000 \n",
"mean 41.744815 7.071000 39.833333 26.860000 1.750000 790.472973 \n",
"std 6.269069 3.044779 4.910261 6.393638 14.256577 3299.459447 \n",
"min 28.200000 1.800000 35.000000 18.000000 0.000000 5.000000 \n",
"25% 37.200000 6.025000 36.475000 21.250000 0.000000 40.000000 \n",
"50% 41.900000 6.600000 38.200000 25.000000 0.000000 90.500000 \n",
"75% 45.900000 7.812500 42.700000 31.000000 0.000000 297.750000 \n",
"max 61.200000 12.300000 47.500000 44.000000 172.000000 34603.000000 \n",
"\n",
" Tumors InPritts age_max \n",
"count 148.000000 148.000000 89.000000 \n",
"mean 1.750000 0.905405 61.638202 \n",
"std 14.256577 0.293648 13.414371 \n",
"min 0.000000 0.000000 34.000000 \n",
"25% 0.000000 1.000000 51.000000 \n",
"50% 0.000000 1.000000 60.000000 \n",
"75% 0.000000 1.000000 71.000000 \n",
"max 172.000000 1.000000 96.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kq3_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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IhYjsHAeAElmHxcWZiLqPrjQAtKmpCWfPnjZ7+pKScxJm0/UInc7odVBZqWr3DKOv76BW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\nZxERkQTzA112CP96r/N6AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a7cc2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"kq3_data[[c for c in kq3_data.columns if c.startswith('age')]].hist();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Breakdown on studies by design."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Retrospective 83\n",
"Prospective 38\n",
"RCT 26\n",
"Pop based cohort 1\n",
"Name: Design, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kq3_data.Design.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I will drop the cohort study, since it is the only such representative."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"kq3_data = kq3_data[kq3_data.Design!='Pop based cohort']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"kq3_data = pd.concat([kq3_data, pd.get_dummies(kq3_data.Design)[['Prospective', 'RCT']]], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following [Pritts et al. (2015)](http://www.ncbi.nlm.nih.gov/pubmed/26283890), I fit a binomial random effects model, such that event probabilities on the logit scale are normally distributed with mean $\\mu$ and standard deviation $\\sigma$. This distribution describes how the probabilities vary across studies, with the degree of variation described by $\\sigma$.\n",
"\n",
"$$\\theta_i \\sim N(\\mu, \\sigma^2)$$\n",
"\n",
"the expected value for study $i$ is then inverse-logit transformed, and used as the event probability $\\pi_i$ in a binomial model describing the number of observed tumors $t$:\n",
"\n",
"$$\\log\\left[\\frac{\\pi_i}{1-\\pi_i}\\right] = \\theta_i$$\n",
"\n",
"$$t_i \\sim \\text{Bin}(n_i, \\pi_i)$$"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Applied log-transform to ν and added transformed ν_log to model.\n",
"Applied interval-transform to σ and added transformed σ_interval to model.\n"
]
}
],
"source": [
"import theano.tensor as tt\n",
"from numpy.ma import masked_values\n",
"\n",
"model_data = kq3_data\n",
"\n",
"k = model_data.shape[0]\n",
"tumors = model_data.Tumors.values.astype(int)\n",
"n = model_data.Population.values.astype(int)\n",
"X = model_data[['Prospective', 'RCT']].values.astype(int)\n",
"age_max_norm = ((model_data.age_max - 60)/10).fillna(0.5).values\n",
"\n",
"poly_terms = 3\n",
"\n",
"def invlogit(x):\n",
" return tt.exp(x) / (1 + tt.exp(x))\n",
"\n",
"\n",
"with pm.Model() as pritts_update:\n",
" \n",
" # Impute missing max ages\n",
" age_max_missing = masked_values(age_max_norm, value=0.5)\n",
" ν = pm.HalfCauchy('ν', 5, testval=1)\n",
" μ_age = pm.Normal('μ_age', 0, 5, testval=0)\n",
" age_max = pm.StudentT('age_max', ν, mu=μ_age, observed=age_max_missing)\n",
" \n",
" # Study random effect\n",
" μ = pm.Normal('μ', 0, sd=100, testval=-3)\n",
" σ = pm.Uniform('σ', 0, 1000, testval=10)\n",
" θ = pm.Normal('θ', μ, sd=σ, shape=k)\n",
" # Design effects\n",
" β = pm.Normal('β', 0, sd=10, shape=2, testval=np.zeros(2))\n",
" # Polynomial age effect with Lasso\n",
" α = pm.Normal('α', 0, sd=10, testval=0)\n",
" \n",
" # Study-specific probabilities\n",
" π = pm.Deterministic('π', invlogit(θ + tt.dot(X,β) + α*age_max))\n",
" \n",
" # Expected probabilities by design\n",
" p_retro_1000 = pm.Deterministic('p_retro_1000', invlogit(μ)*1000)\n",
" p_prosp_1000 = pm.Deterministic('p_prosp_1000', invlogit(μ + β[0])*1000)\n",
" p_rct_1000 = pm.Deterministic('p_rct_1000', invlogit(μ + β[1])*1000)\n",
" \n",
" obs = pm.Binomial('obs', n=n, p=π, observed=tumors)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 2001 of 2000 complete in 689.1 sec"
]
}
],
"source": [
"with pritts_update:\n",
" step = pm.NUTS()\n",
" trace = pm.sample(2000, step=step, njobs=2, random_seed=[20140425, 19700903])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following plots are the distribution of samples from the posterior distributions for the expected (population) probability of tumor (`p_update`), the inverse-logit expected probability ($\\mu$) and the standard deviation of the probabilities on the inverse-logit scale ($\\sigma$)."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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t+9xYTCaOPWok31lDSQWf+tkgpIVyX5wDb8TPpuZanFoNZx979JhyOc2mJ+zv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OH4wGYDBJBa4X/HDS2j6iTEdCrPfY/s4XebDlI2TJYuinDNG1fgC+T42qZ/4VD6CGs6VvOeleuO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\nV3BmocaLd6gk1RSWMJlfa0KoQst8J/fDkS4a6gCjzZMnrPSHDawwomx+3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0eP8spXvhKA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FnPl3HR40jWtE0VEYhE5B6CgJFQV3WkCGjRexkpj821OFpfpB6GPEpk6HUO97eO\nsWDbVLRhSqbOsuVgKHnwTm0Q9i6idB0PISCvqySRx73sl/1tHp9r0R13yYeWgocTF01echcoqmXm\nGhZzjWRxw/N9dDV7TyFZyPFlciVB4DEar5WYCb31sXQi6/USEL83IVmgGLTwpPV4quLi38C+Y3W6\nKS+lBCYKp0/XWJVRdeWVV3L06FH279/Py1/+ck6cOJEhrVjDGl4I+OHUj3nwxGPsGtrJL6x78TNq\nw+q6fPZf9vD9x6ZRFcEVL9/GG39xyxpF+mnEL218GQ9MP8wDMw/zkrmLOG/0HACqRYPf/bXz+W+f\nfYAb79rNWO1iNk+UT9HaGn4e8eY3vxlVVbniiiv49Kc/zfj4+KlPep7g9xhAQX2jcF+o2ZtKkY5i\n0fUtfF/GNOuDav1IZJ/+40tJuye8qKqNMm6OsN/ZjxuybHX8NoudOTYvB+csODOhXMn7zXGzK+vT\n1hSbzMBrJ1PhZymBYmTrVGUZBtO1tqIaQ9F7dULfSN1doO0to6kquqL3GVXRWAQeBKUvNMmPw/8C\n5XVDaR1Nqwv00z4LkQ7dk0wMFVh0Z6h388geo8rDzdS9SsvtuD5p88+TkuOzLcxU8dVsaJmkKzs4\nDnScYsysGBUK7joeG3LbONY9EPfNEEY8iBIZG4OI7H3wfB9FqChx+J8Sj/2g8DZNKFTUITpWmxP1\nGVRFYfu6we9bz/Px1aBwrkBhk3pmeM3gCnuPz7GklsiNhMayGKxadtwu04sWOAb5nIuCEpB3hMab\nrilovkkxZP2D4H4PMiE8X5ILvYQZan4lYQMcqeUQ84GR6fkejnSo5E1Gq0UU1aXYNTJeljTSHsuu\n0x+6F5skEkLTKjZ2FBF4DHVFQwCudPuMqiMzTRpuNy5zIPEzBoIfMURKyUglT8mbTHlgT25URV5f\n09DiBRVDU5FygHWd6VP//vmmRdUMyGkWnKl4uy4GkJuE2LUlWHRwfBtVBGGQ0T2SMnh/RVdyfJcz\n1ldYP1pASth7pI5luxTzeih7tPASjG8UFrtSgeAIveyiaUQhy88F+9+qNMWvfe1r/M7v/A5/8Rd/\nwdLSEtdccw1f/vKXT7swa1jDM8VSt8kd+75CTsvxG7t+7RnlOh2ebvJnN93P9x+bZtu6Mh96x0u4\n4uXb1gyq0wxFKLz97LegCZVb99yJ5SarXJsnyvz2m86h63j8jy88wlLrmdceWsO/XXzsYx/jS1/6\nEu94xztesAZVULfHDemMZUyzraQ8EtFCrBBKXIw0bbSkjYainuQSpVWBIDwqqxwU1TJD+iiGajBW\nHI63W36bnK4y1ZrmcHdfvD19uu362YKaPYrHvqPZYsUZr9lJcqqiulKu58dFWdN5QUqojuiqgq4k\nCluchxTWQ4o8fp70M2MlCeROh/6sVDNZQELJLCUjNQ1fb/Jk/QA+sscICZTj9HjoWqAkBspZcmzX\n9ug4XibPzJcyNiht30ZVAmPsyeP1kDZbsGy3WGgv02w7GEouuhCa0DK1jHw/MQaTb1w0lzwUoYYe\nr8CLFxcvHnBjSspw2DsRnr9yzaSZuoWU0PaWaXkNzJyWCaEMqNVFTLG+klG1d3E/rutj+e1QroRm\nHojb7Ud2DsqQxr83B0hKycGlQyy7y/F5IkzicaWHJ100RadiGigKnLN1OFXwth9R+91Bdb1SVpUk\n8sBEXqzQU6XogQE/gDo8kC55uvweCvvo2a+UdIIpnfS1vnzy76Lf6wkG8oZKMZ+453qHWTL4+U07\nL9N3QRErEzwU8zqudDjafYpp+whNt85sazGgRZdkjKO4zpmw8ZQuWyfLTA4X2L6+km20Zy3pZHXm\nDk034xDl3kWgwKuYLKic7rz4VWmLf/u3f8stt9xCsVhkZGSEO++8k0996lOnVZA1rOHZ4B/33knb\ntXj7+f+eEXP41CekIKXknh8f5b9+5gGmFy1e/9LN3PCbl7BxjSb9OcO64gT/busvU+8u8aUn/ymz\n75KzxrnylduZb3T5xBcfwXFXV6hyDT8/OOuss0590POMo+0DHO7sB8VO8qqCrBeWWjb7ji3F+Ujp\nELOsURV8/Ku5CgU9CsHrUSLC+i09G+NciVrBTG2FDWPF8O9+ZcKXko7tZSiUZY/isbjc7TknLcrK\nCooTKk9WKnHdzDZNi6oAACAASURBVCe1ZqI+aKoSG0bb11fYPBl4T9ywrpMIvQB+6NlJ5PTjfREO\nTWfrAqWJO8qmzoaxIhvXG+g9eVGZGsbh35VcJboQihCUTZ1218ner/DvEX0SVVHI6SqeL2OSAh8/\nWbFHYjuBcnl4usmx2RbL7cDw3JTfwTpja+hBTBmOUsY5VdEtarh1Wl4TXwZta4oSBJL5yazoDf9T\nhMKWoSDE7lR5cBGOLAahlLVSjuFKHiXlcY3GKQoF1VJGVbp928saA+PGBobytfi+FHIaKyb4hPB8\nj6nWLFLKjEEG0JUWC91FjluHg1zD0NskIS7CrAkt8H6dwtvTO+97kdzH8GiZ1GsTCIQQYb00cZIi\nv8k5MgjaTPaE21tyEUUosSxDWhCqNig8tJdYo9dg3Txe6TvnVHB8Dycky4nqXAXynnz8nLC2l9Ad\n5pwpmspUsqAkk/OjxZbd83t4Yn4vlaLB1skKiiLYOFZKZoMIwllPNl9bXpNp+wiO79DuuvhSsvvg\nworHd/1OhlTmdGBVRpWiKJRKiYI5Pj6eqeFxMjz88MNce+21fdv/9//+37zlLW/hmmuu4fbbb1+l\nuGtYQz8emH6Ih2YfZUd1G6874+nVpHI9n3/4pye4+V/2kjdUfu8t53P1q89Y8079FPC6La9mfXGS\n7x67j/31A5l9b/rFLfzCORM8eazBTf+0Z40RcA3PKaSUfOhDH+Kaa67huuuu48iRI5n9z+R71fE7\n1Eo5FsRRPN/N5AM0Qg/sUpinEZhagSIahe7sWdgfKxyGaqSS1LPPwiAlQ6SSukfyySKTpimosXco\nQU7JU8mVGdXX4/k+Q+WElsD1T15U2fclTbeOK50VcqoCRH2JDKHLtp7HzrFNCCHQhZExqvJqcH1V\nJIGJEQuiIgJPwELTivNcIFAyFZTYu5AO/1FXUI4LhsayW8ftcWn15lSlEcup9ed0RPVrc0qeXZMb\n4r7uO77Yc3ZgMKuKwHX9mLExuvea0NFFLlT+k2sHRV5lSEiRbJ+xjwW5YCjkDR2JZGqhHXvMWm47\nNSZgiDy6qlDIaZnQz5PBkcFYV0tGPDC9uX1uOAAFdXULkkWtxPbqVkwlMPRzqXIa1WIubjd9nYeP\nP8X9h/ay4M5k7lPAGBl5jCJjKvFmtbtBzqGuqCs+S71YzcjYrs/+o0t0HS/2VAXhboEHOpJjENKG\nkQxDCJN9oQwiuNfRHDaUHGWtNpCNUekhaEkbhbqmoKoKQ5FnLmXcRyelH5NiuOBxsHGQh2cfDb2t\nKQ/2gNBkgPGQOTPKzyubOtvXVyibeuzdlCnZep+9dipyJU3FH41OTGAiBBvHiuQNle3rg/zEeWeK\nttdi2asjpeSphSPMtLLsk/2LUgO78Yyxqqdp586dfPazn8V1XR5//HH+5E/+hF27dp3yvBtvvJE/\n/uM/7qt077ouH/nIR7jpppu4+eabue2221hYWNmaXMMaVkK9u8Ste+7EUHR+8+y3ZFZYT4VWx+H/\nue0hvvvICbZMlPnTd7yEC84YfQ6lXUMamqLxG7t+DYDb9tyZybkQQvCOX9nF9vUVvv/YFF+779Dz\nJeYafg5w9913Y9s2t956K+973/syzLbP5ntVLugoBCQCUmYZxYK2k3Ch3vC/pt3k+HKQwyCESOUQ\nZONg0l4eCPNSlCTvwFB11uW2AD0ei7QBhMKZQ2dQ0gLlJF2s15N+bBBJCR3fwvKSpPKGs8ScM8W0\nfWRg7o6UkjlrnmPLQfHjKJ9qfW2ILbWNXDJxIYpQMZUiWybKDOVrCCGYKGbDOpUwrynKqepVjoLw\nv+T973qB8aEKwbb1FTaMFgcy/imKGvcvklcR/aZqb45Y1Nem5bIcUnlHOXLbJitUi0moVbRqHxwT\ntBCQEoiByjGE5BU9Xo52x8XquKgp2vik/4HXK6+rsYczIpp4srE/PiaagdF8fLowwgVHUy1kPVUI\nZhaDeZH2aKSvEbEoFtXA+6j0hP+lw9W2TpYpm1m6fYAjc3VaHZeO384YVRtGixAZYOHhfio/bvfR\n2VAGPfssrYBTGVwFvci4sYFm26abyosKxkIJvaZKaBRmDZAkRDXrlc7kCPZMjPT9FgT3ttMTlqj0\n9Cs9tpoqMuMR7eklJ4kM3Cgs0k+NZdoA8vHjcggAF50xxou2DrN5IjCoz9pco1o0KBeMZCFHCPyI\njTQOgXYzY532Zgb9SfoUmmOh/ALT0DhjY5VaMcs60vAWmbKPcKI1zbR9NG6/VswN9OqfTqxKA/3g\nBz/I9PQ0uVyOD3zgA5RKJT70oQ+d8rwtW7bwyU9+sm/7k08+yZYtWyiVSui6ziWXXML999//9KVf\nw881fOlz8+5/pO1aXLXzzYw/DQaX6cU2//UzD/DE4ToX7RzlD99+8RpV+vOAbdUtXLb+Uo63prjn\n6Hcz+3RN5b1XncdwJccXvvUUP3x8+nmScg0vNBw7dox3vOMdvO51r2NmZobrrruOo0ePPuP2Hnjg\ngZjh9oILLuDRRx+N9z2b71X0ufakH6sDIqUcJexWIhP+F3327VAZV1Bipd6XfkYtSCtseV1FFSJW\nfgS9tWdSHpgBpBKRkaenPPVCJEaBBE50DzFlH2Y+TFqPciJt3+6jIo/OObh0OCbfsN2AHSwdDVDM\n65TVGudP7GJ9cXBY2tnDO2N50mMRIQh/S+oCuV6QY1UsBKQX+ZwWK1RpfTmnGnFeRyBvsLOPOrsH\nUf+Oz7ViKvsoVcNMXQtgyo48n1lPFVKwwoJ/dBXSirdlu7i+j6YKauV++jpFKOhqf65LRiEXQbt+\naDw+nYXIYqqIs9JHINE/n3oRGVWj+rpQlmwOnMjMz2juiswighp7dN2M0aAowdnR86OEuWXRfYzm\ncE7NxX0+1DiyovF0qtgIhcA4jPLIIFtrLSKr6PVUlfL6QONeIsnnBOO1MMw3cTcBaQrxAFMLbY7N\ntslrid6iCIVW18VxfXJKPh4fQ8lR1E2KWiETtrhhrMjISPI7HYYcvTci404SEmdE8kqf7esrnLVp\niJeePUHOUCkXjPi5NgyF8ZqZWchJ8kll5rlKj4OXeiCy764gxDLakvY2Ro9q9D7wpU/Hb2N1wxpr\nIaviri1Diaeup53ThVU9TYVCgfe973184Qtf4M477+T666/PhAOuhMsvvxx1wAO+vLxMuZywzBSL\nRZrNk5RUXsMaBuBbR+/licV9nDuyi5evf+mqzzs01eS/3fwA0wtt3vDSzfznq84jZ5zeqtprWD1+\ndccbKOlFvnrgGyx2sknw1VKO33vLBeQNlRvvepz9x5aeJynX8ELCBz/4Qd71rndRLBYZGxvjTW96\nE9dff/0zbq/3m6RpWux1eHbfq0j590ItTQz8iA+X83FyvyQhDYgMkXRORcQUF8vuNeK/L922k4JW\npKKFJAShJyBRRHoly/5yQyMvbfAIRGzcIWUcktNwg2c18vKoIUlCGie6h3h88YnMNun3Ewy8aNsw\nl549QUkvZsJ74vExh2JGvV6vXa2YCwhBejx2kVEVKZYKgdE5MVToy13J5KdENOTReZFt1UMMkdhc\n4T0mKYrcWwcs0/90yJfMGni9CMI4YfN4Vt8SqhwY0hgo8/3X1rTk2Ehpnxgyg/khNNblNpNTzMw5\nA8XqUV57w//iv3tki+aVKz10YaQY3LJECErKYBYpb2HkrbMdj6a3FMuQ9oyqoQHWGy4ZG1Vh+GJO\nSYyqeWuBejdoz/FdHp/fG/8GSU7LMaJPDBqJnj4meVHBviDsL/BUwYaxAqW8zqbxMojE+HJl2pj3\nmbaPJb+loKINxWNg6GkdJbn24Zkkb9BxZECD7ngU1Qpq+ByXtQrnjZ2DqqjxvI6KSi96M0wMFdi1\nuRaG1wXnZNj6wrFstLtEZSF8JDldZaicG+jxHMRcKkQ690wMPDZtuGVfE6l6fyTPX7TNk16fF2q+\n0elrc6T63BpVq6JU37VrV9+gjY2N8e1vf/sZXbRUKrG8nEyEVqtFpbK6BLqxsZ9NiuWfRblfyDIf\nWTrOl5/8GuVcid+97P+gZibz52Ry7z4wz8dufZB21+U/XnU+b7xs209D3FPihTzWJ8PpkHuMMtde\neBX/8/6b+crhr/H+y97dd40//K2X8Gd/9wM+eedP+NjvvpLJkWdeg+zneaz/rWBxcZGXv/zlfOxj\nH0MIwdVXX83nPve5Z9xeqVSi1Upqmfi+H+cNP5vvVaWSx9BVKsU8eVOnogaKa9PKehk2rKvgIlhU\ngu2qrlEuJh//kaESUkqWFZPh4SKlhTzLKcKHvKFi2gbrx0cQbo16SCYxVDMZHSlQXjAxFQMzp1Gu\nBDIUGt04l2eoVKJaK1BqO1Rsn1ypixmGXlUqeUqGgdVWEYaCh0j2lU1alo7rBr+7RoOxsSBsr+G4\niDkPwxQU8onCXm3ASKEWz+eV5rWlN1gOFf2hYpGx0eA42/EoFUyka1Mu5hgayVGcbDH7pAFIarUC\nY2NllCWLcsnEN624z+dUTF6yeT175zs4IfFHQc9TK5osiaxRUWzadB2PnK4yNlZmngJeu0tBNxkb\nK9PoeliupNsxsB2DQsFA01SMnM7mjUMMdXUeSnlPK2WTag3MhhFTkReLOXRNwTQNdE3BcX0q5USO\nppqn5S0zMVGmbgX3W1EEO7eU2N9YwFywMjJXCgWGaiab7CpzqcLItWowJp7vsc6q8KKtVbaMD7PU\n9UBVqWDiWxblihqPccd24/scj0lBp1wx8cwOxZKB33UoV0zMBYtquYApknlhEvytoLDgL7ChOkzR\nMigW81RyQR+Lps76dTV2NIbpYlDM6xQaLhXTZHysTN1yKbQNCoUchmnw+InjsUwCwehoKZ5DuYJN\neTaPohv4QlAtBeNdbuewUt6k4eESk+M1nPlgfGb8KbYNT3Jk6TiK6TMvZ5goV8m3NUw9T6shaHey\n41CumEjHwMekgEFeMagUTYTjYKoGlZzJ+HgFw5JMeyabq0Nsrm0A4Md7ZlA0G9vv4tOJx8kQOkZe\no1rK0ex6GJrGFnMrrdxByqUc1WKZhXpghHU7ORzHCDzYihrP764VzCUBlHMmZsWj3nbwpBs/Z0vh\nvDWFTrmUp5DXuXTLBg7Vj7Kk5Om2VVzHoFLJYzaC90C5YlIoGagGlI0CvvQp5fOMn4T4wl/uUPaz\nz5Sh5nAcgaJKCr4Ryz00bFLuBH9Xa3nGqoGs7Y5DwTRwpKBSMfEUQSUXzOW2o1G2TWqlAmO1MnN7\n7sM0Dap6jSUnuzhbMHUKWvDc6gUF83hyP6sV87R+W1dlVD3xRLLS5DgOd999Nw899NCqL9LrXt2x\nYweHDh2i0WiQz+e5//77ede73rWqtmZnf/Y8WmNj5Z85uV/IMtuezcd+9Ckc3+UdZ/4azrLC7HIg\n68nk/slT83zyiz/B8yW//eZzuPTM0RdEH1/IY30ynE65zym9iB3Vrfzw6EPc8/gPOXf07Mz+zSMF\n3v7andz8L3v54P+6lxt+85JMEuvzIfNPEz+Lcj+XRmA+n2dqaipe7PvRj36EYayimucKuPjii7nn\nnnt4/etfz0MPPcSZZ54Z73s236tms4OhKaj2Mu12l5zoIpFYdrBqPm6sp+21yKMyXMgxZSssNDrU\nlxREqvBlU3Rx/aBo6pzapNnsYFlJ7kHTsbBcm/pim6WGT6MVGFUaEh1Jc7mD1bXB92mGhTrblo3j\n+mzJn8nycpfjU0vMzi3TWO4yNlzG1BQKssZy00JRWzSaOguNBlZYrHSonKPRtFi2O1hhHsSeEwfZ\nWAhW9ucXmliWTbPZYWnJ4sRCm03jJUrOJKZbYHa2edJ5XW9ZNJuBrDnXYlYGx7meT7ttY3k2S36b\nY519VEsGlmVTUqssLraZzanM1i2WW12EtGmm1oSnZ5ZYbLRpdoK2HU2idJdpLmcNlE7HpmN7+K7K\n7GyTet2i2bFwNMms3qReb9NoWsx2FnCkzcJim5ZlU8hpcZ8K7hDzThC2XJctFE3Qbtuxp2JBWix1\nAmNe6iodx6NBIkfbtsGAp04cY37JQ1d0XrR1GMuyaDaszBwAaPs2jYYgr0A1rzEV5jg1Gxazs008\n36PZsFC6BrOiSWPJohEa4G3Hodmw6TgeR2ayrIkRVCTNhsXDjT202y6tbjeWo0HwryoEltrNyPbE\n8QMcnj+CJyXNpkvDDvroOS6zs03yisDquDRtl3bbp+FazM8vU6+3sSyHJw7Os3D4MJbXjp8dgWCp\n3obJCrOzTVodh+NLU6j5Lu2Ow4yzgDB9rLadkaWx1KGUy8XPAcDdj99H1ajQ7FrktTzfqz8YjJvo\nMFFZz4HF7Dg3GxZ4Pm7TwrIcPEWh4Vssu20sx6bl2szONmnabZoNiwW/hekEc2JxsU2767DsLmE5\nSbuO8PGkoJxXsSybrnBpuBaW20X4PorfodEMPKEtx8ZybRSh4AoR98WyFCzLRiBouV3IBX3fXBiK\n5+RSOG+tjs3yssCzXZ46doI9C08Ffh4/R9FQaLWCe6giqRsK3z10mGa7TV4x2bYh8CjPzjbpuF32\n1Z9EExpn1Lahq8F3ea7dzIwxgNdcxOq6lIwiHcuO90/pS/Hfs16DvB18O7qOR9uycaXD8YVZCoYG\nhsasGly32bAwnBZld5l2eI8Nx8UUtbgGH8CS12JopMjsbBPLyb47G41O/C46HXjaFGe6rvOGN7yB\n++67b9XnRB++u+66i9tvvx1N07jhhht45zvfya//+q/z1re+9QVba2QNLzzcvvfLnGhN88oNL+OC\nsRet6pwf753l43c8ggTec9V5/MI5k6c8Zw0/PShC4ZqzrkIRCrfv/TK218849uqLN/LvLt3Eifk2\nH//CIzEd9Rp+/vCHf/iHvPvd7+bgwYNcccUVvP/97+eP/uiPnnF7l19+OYZhcM011/CRj3yEG264\n4fR8r0Jl3vO9mFFPF4EHqqINUVQrjBnr0FSFTWNl8kaWvWtzZRNjhVGG80MpuuSAfDmNKK8qYgqL\nLy8CL9ag8L/ouLhejOvH5A5jxWEu33kpF27bGLRPFAKUGHpJXlPyHHqp5PqGHYYHepITC4Fyv9Do\nIOKKVCdHOizH8ZIwqSCsKtjXlZ0MqURVG45l7Q3/izDfWYiPESEdfTo8KILSE2YUoTfnyiMiqZB9\nx2+eqKTO80HCaDXJgbFsN+6nNiBkMNp3dPkodRHkZeUNLUPqkzleSXLvcoaKENnrpfud/hdAC3Np\nZhazinCm/dTxUsiBoYubJ0tcctZ4ZhyiaTGzaGXCE3vvTXiV8FrJ9SJK+t6IqfT5Ami69aCXEo7b\nhzLEDHE/FbU/RDLK8+m9hpSYPakBRuhN1lK1y4QQjFbNOEwxCaEL/m057fib5ktJy2tiyzQhgxIS\njQTEKmqK0CaSRh0Q1pnOMQJwvVTZAZSY+KOcSyI7MsyWEdmKvRxfa+eGIO8o2uf7kmbbwXLtUA4N\nTVHjudR223TdLi2nxYw1F7d3uNGf4zrVPcKJ7pEMMQxkGQAtt8NStxkTxvQ+b3HYbToct2ca9Yb0\nedJl27oKru9yoj2VqU32vIT/felLX4r/llKyb98+dH11q8QbNmzg1ltvBeBNb3pTvP1Vr3oVr3rV\nq56GqGtYA/zgxAPce+J+NpU3cNUZb1zVOT96Yob/9ZXH0FSF/+ut53PW5qHnWMo1PBOsL03y6k0v\n518Pf5t/OXQPb9r+ur5j3vrqM1hsdvnh48E9/U9XnptJdF7DzwfOP/987rjjDg4ePIjneWzfvv1Z\neaqEEHz4wx/ObNu2LQkNfrbfK9u3KZs6OycnePzAMlvyZ2YUuyiHKVYTQiV0JD+EqoyG+yIjRvYx\ng3X8SBEWGaVQIDBzGnlDh25vnkxv3ovE8fxYlrR8HbeDJ5UME50iAn0guna5YMR027bnMN0JPDSL\nYV4DQKvjMm4qfcrxIKSP6XrdzPaI5W/WPs5IPh/3RBFBCN2De2dxPH8AUQe0HCuOnomU35MxvQmy\nyly8PWy3og5Td+digzI9bmeOr+PBo0GRZV9KEJJq0aBU0HnqeIOonpGUQUHhSWNT39Wja00Mm1w0\nNoauKXgr1O5TUjlVhqawY301c5d7e5nOSRrWRxnKd/ELQzy5dDDePqpPMheSkqTb8kLFuDdzJmKT\n+5UXvZgfHHmUuaVOPKFbHYdiT+7WSogNP0Scq9SLQSQsMb8DgYLeO9cUlJiMIY2IIMFyskZlusht\nSa0ypI8yWpCUxAj7F5sh2YbgjA1Vnpw/GsscnBv82+g22Lu4n3NHz8b3fGZSuVOBTCqedBHhtYQi\nkH5sPQD0fOey/Y4ylDrhc6ILA0UJjLPN42Vecs4E9dBrGRmiGbKb1K9YdhKaFAFYXmB4BWQXCn50\n/1MLLe1w7PbVn6IXiqLGz9Dh9lMZgzhNFNPoNmh0G6Fcoq9wcnJ/k7zK7Gj0563OO9Psng8KqC9Y\ni4xV83i+pNm2+x/sZ4lVGVU/+MEPMr+Hhob467/+69MqyBrWcCpMtaa5dc8Xyat53vWi34zdzCfD\nDx+f5lNf2Y2hK/z+1Rewc2PtpyDpGp4pfmXr5Tww/TDfOHQPl05e1MfoqAjBu954Ds22w4P75vjs\nv+zlun931mmvir6GFyZuuOGGk+5PU6E/3zDVAiU9h+13YsKJUs4ElldkXEsnXwe/k3mdpkuWPQUr\noyR8XdEQIlWaILxMXg+Nh1Dx31HbxpGZn5BTEi/GvmN1NEXB0NNMccF1luwlZrpzDOuJh04ogo4f\nKGo5XWVyKFGW054fx+tXiHsNnUFIK0YZCnSRLXacfvQV1Dg5PWgjKSQcFslh2Vkmr+bC4wM2soFJ\n9eG/UaheQSuwwCIlvRjKEeyvaSPU3bn4uLKRhBEpQqGi1Wi49dAwUOM+9PZRVxX00AuybrjIiYUW\nAkGtZFAMvY16yAAXFVLeOFZkqeUEyiGBN6OXevtkyIydUNlR2wrtpYxRVdZquNKl7s716PKh99KX\nMVtkVNsIoJIr9RTIDeaJ4isxC9zEUCEcsyKLncWMzIJ+fbfX+E0bhQvdhcy1ABzPzhBhBJ5LZaDX\np9d7EiFN2qIIgSZ0tlYm41pzaZnbIfmDLqK8r+zCBGTZ7SKoQg3rvEXGGPg9xnyGlCOUXxVaTGYi\nCAgp6sxS1UbieZDTlXjeBO1F92QFQ7XnneP7ARGELYN32JaxEVyxhO97IelE0k7HC/qoIPpa31Ta\nwPH5wDDzZZZopddwSpA29gI07WZGTl9mizRXtGEsP8mPjWVzO3EB8XR7p7sG5qqMqhfSh2oNP5/o\nuB3+9tHPYvsO7zr3bYwVRk55zn2PTfG3d+0mb6j831dfyI4N1Z+CpGt4NshrOd6y81e58dGb+ce9\nX+Y/X/CuPoNJ1xTec9V5fPRzP+ZbDx2nXNC56pU7nieJ1/DTxKWXXvp8i7BqbCpso5yzmLaC+ky6\naqAqJ2cZTUL8gt9ZBTkJ/8MPtkcKO0C1aGCoOkIkyl4hpMCOPTKhklLNVdhW3Em7k1V9XN+noKZo\nsyPVQwSGWy+7VqRoFVP5jYudOnktP1ChT7O+nQppg6M33M32E89Vmtq731gVRDqxoejYnh0buIEc\nwVq8P0CxEkrAvlY2AsNzvDBKTjWohEZTbwhdRKc+aWZDy6OaTYESG1FuByx8c0uJLJ4v4z6b+ei+\nCUZrJp12NqcnquVjGhqmodGynFC5VBgUzlTJhYZeivI7LTuhZIvNLvVmNzQU5+N9Q/oolr+cUZVb\nYW2uuXoHTdHRFIXJWg+BUKz4Rj8FApWX7BrHcf2Y0W7MHO0LF4tquoW3iIa7GDNkRlBTBs/h5aRg\nd5pbLq28J0ZLcp6u6jieExuqaWwor1/RQ5SZw+HfrrTRhI6hBEa7rmixMQ+BJ2eQURUZX0osn4j9\nLy8aOhvTVGg0kjlaVodwpcNkcZyp1ixIP3hG23k2589AFRo2/YZF0HY4Rqk5n/YUZeaPiKj3oesH\nBpOhqPjh+E23Z0kbPlGIY0E3aXSbDOWHWOwsIoQShl0OGkkGjn3vMb2/onsYhR8rQkETesBCusJy\nghjwI11s+HRgVUbVL//yLw9cCY4qlv/rv/7raRVqDWtIw5c+n9l9G1OtaV698eVcPH7+Kc/5/mNT\n3HjXbkxD433XXMi2datj61rD848Lx87lnOGz2L2whx/PPMIlExf0HWPmNH7/6gv475/9MXfde4ic\nrvLGX9z60xd2DT9VXHnllfHfjz/+OPfddx+qqnLZZZexY8cLz7BOr4gPKowLMJKqj3ey1dP0CrPr\n+WhCzxRZHa+ZaIqWUfaiIr5RboXvw1nDO8N6RgaKyCrrvXImIVgB0mFLtuPhh8ZNrZSEXp5oTbO1\nsmmgmySSdzWe5bQcsVEQwlQKsZdMVQS6qjBeqkFPVJwilJjFUVU0CI2RNFV9UHS1X9HN6SoVJhgL\n2fgUoTCUTyIdep1tthspd9kdZ2yo8aND833XqBYN5pcCQ3W4nI/rbxVyOuXQSFUYHCrZm3MqQsVd\nEcrAedZbuDjJS0mOkVKy58hifMagNgbdtqI/RjlXQhWCc0Z29Xi/4sYB8DyfvGYghMhQhAsh0BQN\n13fJGym1VAT/8/GYd6b7jGZFgOu5mdyiiKY+OlSkblSUY5Vup5arMtuew+8x3HfUtjGUr7HcTZTu\nQbTx6XHxCZ7LCKqiBt+y+YDsbV/9SRTRn37Qu9jQcTyMcAFGVw2KukFTtDPHj+iTFPQcQ9oYkina\nXS8IRQwNz5UMCyUO/0vGbLY9l+pX+p1FX8yoqqpxDtbR5jFyWi4+T0ofz/fwpI8Qgh21rbSdcTRF\nz4RiBuUXkjYtt8Op0Dv3EqNqwLvy6eRJnV5H1eqIKt785jdz5ZVXcsstt3D77bdz3XXXcdFFF3Hz\nzTfzmc985vRKtIY19ODrB/+Vh+ce48zaDq5cRR7V9x9dM6h+liGE4K1nXoGmaNyx7yu0nfbA46ql\nHO//9Qvjj96BRgAAIABJREFU4sDf+NGRgcet4d8e/v7v/57f+73fY2ZmhqNHj/I7v/M7fOELX3i+\nxeqDoqQVlMGf2/T7KV0wtxdpT5Xn++HvYNuG0SIjZlCbyk5RrRsh+UEUwqRpSehTpHynC/0GMg9Y\ngh+AnKHh4TFSyWe8AXIFzw8QK3wrGZiZ9kNlTVN1tpSzuUZVLYlUiOTdOtYfvaCgJHWmYGCB+ICi\nIGvwCCH4xS3nsn19la3rBrOC9Ro7y1Zg6HTsrHKuhcqxn/JURRjTNyClxMypVEs5No6WOXtLDTOn\nsW64yIbqaCb8LDLM4rphUT9DUVSRJWGYLE6Efey9H4lHJGn7VNplMN+KRtYbNWIOB/dVBB6KqJ4Y\nJGPkeD5dx8d2fcpG4aRXOWN9lUvPnkhJGRhJuqqwaaJITo88fxJfutx39EH2Lj6ZzFQZ/C8hYun1\ncGSfxZWey5wazb/E+Iu8NdBDkiGIjfP0QgcEYxLB8Rzq3mzftSLqlsE5YlGuXr+MamggnlHZwUhu\nlFyqLMBKT1g0HmP6uoFHZcL/FPryN3VVyxQcT3t+AQ43j+H5Xpy3VtALGKoekOjEeYhDPUaVFbb9\n9Bh9FUWNPX+BYR0Zuql81Yx382kYW88QqzKqvvOd7/Ce97yH8fFxhoeH+a3f+i2eeuopNmzYwIYN\nG55rGdfwc4xHZh/jqwe+wXB+iHee+/ZThs/c88ARbvxqYFC9/9fXDKqfVYwXRnnD1tfSsJt86cmv\nrXjcaNXkD665iGrR4Ja79/Hth4//FKVcw/OF2267jS9+8Ytcf/31fOADH+D222/n7/7u755xe91u\nl9/93d/l7W9/O+9+97tZXFzsO+amm27i6quv5m1vexuf/OQnV9VuOiF+pdXTtNJ8zvAuYHBB2DjM\nLCyCqalqrABpqsL60joAKilmq6gI7eRwgWrRYKSSFD6NlEJVVSgXjNR1UtccECYWoVrQ2TJZYric\nLaYZsBP6A/sQ5ZqsJo+hpBe5cPw8zh89p++9n5Znc3kjJaPEZDHJ99oyERhCveGTeW1A4c/e4r9A\nXjMpGyXGa+aKRDi9ymaEWjlLmBIZVRJJTyocutAZ0gNDTwE2jZfj/Jctk2Uu2LqeF68/j2ou+I7F\nRpXnZNjnImVVF3qWqKQnnLTXqIs8mL0YtFWQzI0dtW1MhvlQlaIehtv1n3Xe8LlAEFZ4eCbIhTHU\nXN9x2QuJRLEXKeIEBQxViRkyARad4Dlt2s1YOIkMDZto0SA5PiJwgMBzWdSLA72Uwf7gPE1RWDdS\npFo0Ms9zr7EWhagOIsFIo+0ndPU5PSKoyXqEdVUhJwIPdlQ4eaAHMty075BFc8HMPqcrLFxE/S9p\nVUwt37c/bZCoioInJZ0U066uqrGxnkZRD+bDvDWPJ72+Z7akF6lqw6zLbaakVRkqmZSMoM5YFM5a\nMbILGNtrW/u6kza8FERs4AWet2gOpOQVaaY/Bv59OrFq2qx77703/vuee+6hWHzmxTfXsIbV4Gjz\nODftvgVd0fk/z7uOslE66fHf+8kJ/vqWH8cG1dbJNYPqZxmXb/4l1hcn+d7xH7Jv8ckVj5sYLvD+\nay6kZOrc9E9P8M2Hjq147Br+baBaraJpiVJZKBSe1Tfplltu4cwzz+Rzn/scV1xxBX/zN3+T2X/k\nyBHuuusu/vEf/5HbbruN7373u+zdu/fUcubKaKESIFbwDKVRNPJMGBtotu2+RO9I2YmSwzUl8VRB\nkv+0YTQZh8hgy+sa4zUT09AybGYQ0rCnrzOAHGMQ8loeXRcoPcqTL32mWjNBCBaC9bkt5BWTglqK\niS66qyyHoCnaip6ECEP5KruGdwb5K5FsRhQClWEp6IMI87G6XjcIDwxxRm3bKWUbTAcOk7Xsdyr2\nVEmvz1q+YMcY567fwnkTO9lY3jDQeM3r+fie+VIipcTxHQw1Md5i41rRMoT1Sk8uXRz+d5I+jFZM\ndm6qhMdlDTRBwFSoKRq5FP2/TCcxpfuu9hsYm0ZX+C7HOWqpTenrDzDwp9pTvacDUFQT5VxKn22T\nwe/1uS3xWF04di5nj5w5kE4fkrFThEIprwWLEvoEo9UwHDQeu4AqwpUeG8aKfc9XL4YrKSVfCMpa\nLaFgVwQFvcDLtp/N+et3MFY1GQ7Dg6O8xShPMujbytcpakVMvcC26taefqWf7/5nK70/qgUZeV8N\nTaFSyMVe8TQ2lwMHi6mbeNLvGwchBNuqG8krBfJqjl/aegmj5kjYj6Aj6YWRaq5KUU/eZVFfdw2f\nmWozCN9NFmkC2XNKAVWojOgTmT4OGq7TzFOxupyqP/uzP+P6669nbi6Iu9y+fTsf/ehHT68ka1hD\nCvXuEv/zkX+g69m869zfZFP55B7R7zx8nJv+6QmKps773nYhWyafu8Kja/jpQFVUfmPXW/irBz7J\n5/d8gQ+85PdXDA/YMFbi/ddcyF/d9hCf+foeHNfn8hf30hOv4d8KNm3axNve9jbe+MY3omka3/jG\nNyiVSnziE58A4D3vec/Tau+BBx7gt3/7twF45Stf2WdUrV+/nhtvvDH+7bouudwpVtwJlJayXmLR\nW8QJ82DO3T5Cs23TslxGqtmVYiEEBbXMlvxOdhSGetoKFAbL7SSkBJHroyekabxWyHhf0l6NKPwv\noRWnx7uRkucU67m+9FCFgi/8WDvxkTS6jSBXSB8np5isy23JnOcOYAR8pjC0KE8r2RZ5ABShxEQB\nkUEQITAGBZ7v4gGVXCWmcs6pp6bnH6nm8aRkadnGlTtwpUteNfreUZHnSQ6op1M285RXwTCe5I/4\nNOxlkBJDNeLQ6MjjZHWyTGhBSlIqNLNH+RzEwlgu6HQjD0ovG4MIPQJBAlgoUxBAOWiuDLLJq6XV\nlz4QIpWHl5JVFRqedLP5f+G/Ug7wm8UEEGrcTtQ3dwWSBDVlVG0qbyCvmZiThdj7GxmkIpSt63UD\ndsBTqNVpo0gRgoJSpBOGFQY5USrrK2PQY3uaOY1LzhxDVRR++ERQrsBbwVsK0HV8Lho5q297+p6o\nQsE9iVER9dV1g+e1Vs6jKgrC77+xmqKR1/IxJb2u9xuX40MmZl6jEnrGexdt8mqeSyYuZM5aYDhf\nw0fGPB/R7ddSxpoqFFzfTdhSw7uuCpXN+Z3BONhJyGbaKzmUG2apNcXpTqpalVF17rnn8tWvfpWF\nhQVyudyal2oNzyk6bpf/9+F/oN5d4oodbzglMcU3HzzGZ/55DyVT57/9p8so6at2wK7hBY5t1c28\ncuPL+NbR7/H1g//Km3e8fsVjN0+U+S+/cTEfu/VBbrl7H47r8yu/sGXF49fws4tt27axbds2bNvG\ntm0uu+yyVZ97xx138OlPfzqzbXR0lFIp8DAUi0WWl5cz+1VVpVYLSAo++tGPcs4557Bly6nnlhBJ\nfZmoYG9OV8lVTUYHkJEqKeVPV7Of50hhWOws4ku/r0Bo2qjavj6rkQ3KIUkY2bLKdUbROYmnSiKx\nPZucmsMhUUzd0HiUUg7I5QnguM/eqFqf24Lt2+T0SElPZM0bGooQjA+ZLAo1pmweNUeodxtBaF9h\nlL0pD3hezdF4GtcXQjAxVEBXFRaaHTShow1Y+Y/uoy+Tks0RCcJqEd2TE61p5q2AlS/tmSubOrpT\nDb1JGSkRpI2pbIHiQV7T0Vqeo8uyz0QSqbaUhBcyIYYYMFX685mUFcMpV5ppRbWM5bfi/ULAiD7O\nnDPVb/SF8igomQZruQpDekij33OhWq5GoxvSdCsqvu+hq3rmmZko9hf7Ti4dGOsRZbqunHqxJd2G\nn8oFUsRJH7nYQD9v+wiaovDU8aW+Ywo5jXZ3JYryrHdSU1S6PY66TEHocH5EZRGiPYNDERXyWj4e\nB03pXwAVQsQGVdBO6r2kqPG1/3/27jw8qvJs/Pj3zL5nT4AkBEKIbBEIqHUBoUq1iiuhCBa1tYpU\neVsXLuryo7bVghX7thV5q0UFcUFF3CjW3aDWhR0Jq7IHCNkzM5l9zu+PyUxmss2ELBOS53NdSjJn\ncuY+z5yZc+5znud+gtWdJdlPToYZt7fl+fMkSYEfOVQ4IzxBSrXo0WqU1J3UYfMF2ilYKXCgJRu7\nrKZcaSdZEVmts6NiSqpKS0t56KGHKC0t5aWXXmLu3Ln8+c9/Jisrq1ODEQSf38fzJS9z1HacCwec\ny5SBk9p8/kebjvLyR/sxG9TMv2EsgwckUF5u7Z5ghW5xde5l7Cgv4YMjn1GQNoJBloGtPjcz1cjv\nZhXy+OqtrPnsB+qdXqZdnBtTtTHhzNHeO1HhioqKKCoqinhs3rx52O2BEsR2ux2zufmdbrfbzf33\n34/ZbObhhx+O6bUiJqKMoZ9J+G56qtqB2dA4RibiREZu7I4VeJ2Wxz2Fy0vKxeV1NT4vdKeqyWl4\n2K+KsPU3FRyg7vK5AifbTZb7ZQKlnlvQGWNdtQo9WoW+cc6n8LgVUqjYQW25Ah+B90KpUHJWcl6L\n61MpVKiVmhbHmcRqYEbzLuqhO4P4kWi5zaMJnkgGE6rAY41X7DVqJa6G5yTpEjhuC5TxD757TZPb\n4H6TbNGRnW7G6fZSXhO4wxBMetQqBT5fC0lLw52qxt2o9W1qmsC0ND9UWySpMUlRhO4MSQ3/V0Ts\nl8GXr3d50aojJ4DNTxpCTVlZQ0yRQaUbUjlSFyhylGnqj1qhIkkbPeFtWo4+WJFRLcVebEGhUCD5\nI6v/xVK5zthQ1bOlb5T0JAOHTrZ+eSA8brVK1axiJgSSSIfXQY1sRQLSNVlUeE6Qpo+ciDycSlKS\nY8mmxhmY4iE86W81loiCEs3vbCkkBWMyRqCSVGwv39ks/sB8Z/7Q8ACX3HhXSqEIFMawqJJC81YF\n99UErYUabz3pmkxqa0Ee0Hl3q2JKqhYuXMitt97KkiVLSE1NZerUqSxYsICXXnqp0wIRBFmWeXnP\nG+ys3M2wpKHMyL+uzYPPuv8eYu2GAyQYNdw3c2zEWAKh99CpdMwe/jP+se0ZXtj1Kr875zcR4wma\nykg28LtZhTzx6jbWf32YaquLX1wxLKIggHBmW7lyJU899RRWa+ACSnB6j927d5/W+goLCykuLqag\noIDi4mLGjx/f7Dlz587l/PPP51e/+lXM601LM2NXG3Gq7KgUStLSondLthwLnBC5ZfArlfRLCXyv\n1TkVeCoDuZBOr8Fi0qPwGrA5NZjMuqjrTiNy+fFqJ7JSSaJJi0atxNtwopSUaAyty+/3Y3bqUbt9\n5Hn7U+o4HPp7syXQb82kMVLvcTQb8G91+VAo1Vh0kf3bLi6MvBgbS5s0VThCoqyqnpG5KaFqcF6f\nH8txa7N1JnuM1HuUJOoMzV4ryWdE6QqcUKUnJzDaPLTdsSg0Kk42zDfVv19CaAxMkNGtIiXRgMav\nJj3VRIW7npQUE8n62Lc7JdlEvTLyYmGyxUC9MtC2siyDS0eq0cjA/ukccQfep4REPTalAa1KQ1qa\nmXqPCrNbT6LJQFpK4PXT0sycrLSH7likpZmpUehJSTJgs/uxmAKvUe/QotTJWCw60tMsWJwG9DUu\nEhIM+BUKDFp1s/Z1e3zoDzR+V2tVzZ8TlOA24PK6I2KrdfmocbmoUWgwm7WYLXpcftCo9Tid2tA+\nGPxXXxVIDE0aLU63IvR4WpoZS2ngc5WeZiapyXtkrg88Lys9hUR97PNZWsy1GCUtWf2MmBO0mP16\nZL8RjUITsZ3B9Qfpqxxk6QdhsHiR7RZqPdU4XBosFn3E5y+ahGoHNBm3lpFmJqt/Amq1MjQmKtgG\nAE63F8upQJKRlqLGZ4ssZ56SYmKoPvAZ/eLwRhLtblLUCfRTpDE8O5m0JANenxezK3Kb0tMDF0oO\nOAOPZyQlkmZpezs0TpmTvuD3SPPPZ0DgsQOuxvcyqFy2oHAEvnf0VQ6UKLE09KdNSjKg16qodXiR\nPBmYLY3zdqWnWaitJ/SdJ6tiSoViEtOaqqurueiii1iyZAmSJPGzn/1MJFRCp5JlmTe//zdfn9xE\njjmb2wpmt1rpT5Zl3ig+wPqvD5Ni0XLfzLGhmdmF3ums5DwmZ13Ep8e+4O0f3mN6/jVtPj81Uc8D\ns8fx9zU7+KrkJLV2F3deV9BN0QpdbeXKlbz11lsMGDCgU9Y3c+ZMFixYwKxZs9BoNDzxxBNAoOJf\nTk4OPp+PTZs24fF4KC4uRpIk7r33XkaPbj6HWriKCivVtnqsdgcKhTKmu+h11sZ5cU6cVKJsmFV2\n77FyDpU1dvexK9woPR4cbjc2q7Pdd+htNid1VicaCRyKxtfVqyTKdcEiBDLWOgcujx+lQ4nDEajS\nJSFhrQs8f2DqIMqs+5rN82Ozu9B5TdR5IifXDI8zLc18Wj0LNEB2sp66msbpFnx+f2gbwtdZV+fC\n4XEguVSUKyJfq7bWgc0d+Js6hROVs/2x1NpcodetqrLjc0XOIeX2eTBplagkLy6nG6vNQZWyHp+2\n7YIGQWlpZmqqHVhtke1Y5bNjtTc+lpmSwACTkfJyK3abG7/fxylvLXVOJ2qFl3KVFYfXgbXOgc5b\nT7m/cVtlnx+9SiLZoqO83EpVrR29WoETGZstMIbP7nYjexzU6HRUKe3YrE4cDjdVVXbq6t14dJ4W\n30uDN4lKT+Aukaym1fe7ttaBx+dG42mM7UhpDQ6HF9mtxKXyYK1zYLe7cDtdODyB380WPdY6B2ql\nOrR/2jxO/H5laB+tqLA2vkfVdrxN3qPg86pVDjya2C++1Vkd1DvdeNwaKqtteJxuElRK0hJ0EdsZ\nXH+Qw+HGiwKTIYnjNjt2rxuHx43V6qQGB+XEth/W1DiwOiLL61dX20m26PC6PDgaEqbwz5nH2/g5\nsdT6I/YhgCqlPbRvWusceNxebC4XSslHdZUdyevD5/c126bg+o0+C5XOatBqKHe1vR02T31oPbJG\nSbmq9ecHnxferg6rF2t94HGHw42ERB2N32N2lSLwHvmcKMPiraywBfZbe+BiyPY9J/nJBbltxhqr\nmJIqnU7HyZON/Vc3bdqERhP7YENBiObDw5/x8dENZBjS+fXoX6JrpQuGX5Z5+cN9fLKllIwkPffd\nMLbZYG+hd7p6yE/ZVbWPz459SUHqCIYlt31V2WzQMH/mWJ5+u4Rt31ew6MUtPHz7+bGXPBV6rCFD\nhpCamtpp69PpdPz9739v9vgtt9wS+nn79u2nte7w+aVikWTSUm1rnPvlVI0Dp8vbbBySjI9xef3Z\nfLLitO7CDupnRq1SkJVm5Oipxqu44QUuWuspoAkbN9JaUQdZljGrIrtRjchpXjWss7TWbUoZGkPW\ndvurWxgDEtsLt1zkI0ijVGPRGXB6naGiCO3tjRytAiKASiWFCguoFSpcfh8evzeym1pwHF2TtlIp\nFRFdMmVZRilJZKWZ8NUocbgD69FpVS12/wtWemzJ+XlDKN7nxOqrjWl+svBObT5f4LX6a3OwKg41\nxA5qRfN9LlGbADROYmtUmoCGiwBRKloa1AbqPfUxFShpSpJAKUmh8TpDBiTG9H5BYDqD8Pg0qpYn\nb25NW5OEtyZ8SFsscSokieBcAKEJj5u8Rl5SY0KSZR5Apql/TF1cFe04Gp+dNqrZ3fDwojD9kg2B\n78iGfNnh8hJMcVSKphVPFRFtF2s10ljElFTdf//9zJkzhyNHjnDNNddQW1vb4gFIEE7HZ0e/5O0D\n75GkTWTemF9h0rTcjc/j9bN83S427jlFZpqR+2aMIcEU+6BQ4cymUaq5ecQMlmx+ilW7X+N35/wm\napl9rVrJndeP4uWP9vPpllLu+Vsxv752FPnZsQ8SF3qe2bNnc9VVVzF69GiUYd1fFi1aFMeomgsf\n9xRrlakkc2RSFRyMrm1yvuf0O7Do9Zj1avTq9t+p16iVoRPpiOFaLc6RFfl7+NwvCknR4gm1UW0E\nb2SSaDF24cXYVs7hwivnNWX3NCaTxtNoQ2gylqu1JFSpxul1cqq+vCHU9mVVHr8n6nPCk0azxoTL\n60KtVCF5GkuqWz2BAf1aVdvHTTls7Fdaop4jp6yB3iCaQEn3wHseaHJ/8PmtbJLZoCHRqMVaB0Zt\n66ecLf15+DYFC34kaZPwetTNTtpN6sZjQWvzZkHLZeTPSsrDK/vaPfnsyEHJKKorkJEDk9BKUpuJ\nSroxjVP2xsl/VQ1VG32yh8H9LQ37TzuSqhYea6miY8TysHZruTdQ8zGWTdsyfBsLM0Y32+ZYxwyG\nx9JaafsgTQvvTbIuiVJrYG5Ks16N1y9DTePy4IWoYIGPUHxIzaas6CwxJVWVlZWsWbOGQ4cO4fP5\nyM3NFXeqhE7x2dEveX3/21g0ZuaNva3Vikj1Ti9L1+5gz5Ea8rMSmFd0dmiwptB35FiymTr4J7xz\n4D+sKHmFO8fcGvVqm1KhYPZPziIr1cjLH+3n8Ve2cuNP8pk0RkxcfqZ69NFHueqqq3r05PPB84XG\nCVhjS6qaDoAP8vki/z54QlKY0XYXxPZqK85M7WCc/nr0WiXQmJC0dBLVL8mAwWsgM9VI9f7yZss7\nW2uncW0lVcFtTTOkxnx3oa3Xbe18Vurg/XGLxkyZ/RSJukQGGDM4ZjtOP2MGZfZTQCBJCp/jZ6A5\nC4NKT6o+hWpnbWg77Q0l2BM0bY91Maj01FCDWW1igMVIaoKOk04vp+z2iElWkUD2B8tZty4jxYBX\nbSTB0Hr9+NDnJOyx8AmWz0oaiqR2ITuNHLZZm99ta1IYITAptYum9C0kdkqFEiWxdccMZzZoMDpU\nuHzuhoqcba+j6fJg9UW9woQkBWJtT8Ld0kc1Sk4V8VmNFm/w+eHVCZu/3unv2xFzSJ3GhFFapQal\nQoUv7A5w+FoSzVqqrE7SkwxU+6sCr9lQZVBuoxx9R8SUVD3++ONMmjSJoUPbP4hTEFrz2bHGhOq3\nY+eQYUhr8XnVVhd/e307R0/ZKMxP4/arRqBpYQ4EoW+YkjOJA7WH2Vm5m/UHP2Jq7k9i+rvJhVkM\nH5LGn1d8ywv/2cuhE1ZunDK02VUsoefTaDQdqgDYHRor87W30lvj80srGku7u7yRV3JDE4Z24KQm\nKPzkta1zG41Cy5B+qaCxcaK+MakampjLUWspRrWRMntg/IxapWRwWuBOWHaaCXcnlFFvS2tXx4Pd\n/3ytVCKEDnT9a/K6rcXQrMJiO/eJBK2F0WmjQndS8pMiKxiOTBnWrGx+esPxNFAhLfCmBqs/apVt\n36nqZ0xHr9KRoA28fxq1EsnZOL9ZoPufAp1kxOb0NGxT61TKwAS67enaBqDTKLE5A++bWWvApE+g\n2h9IPtIS9BB2v6FpqfbMVDPH7Y1V8HIHJDRMmN3ZAuv0y/7oF/hauaMT3p22fW3U/MMa7U4VQN6A\nBDRqJT6pvtmypi+vkKTG/beTq+hKEeXRT2/dCkkRKmCoU+oIjpzyyzLpiYE7+X6Fm+qGwpmN3YFP\nM+goYkqqsrOzuf/++xk9ejQ6XeP4lWuvvbZrohJ6vY+PbGDt9+uwaMz8ZuycFueBADh4oo4n39hB\njc3NpLGZ/HxKfkxfGkLvpZAU3DxiBos3/p3/HPqYwQk5jGxhksOWFOSl8v9uHs9Ta79jw/bjHC6z\ncue1o0hNjGEGTqHHuOCCC1i8eDETJ05ErW48IT7nnHPiGFXLgiW6LdrYKnq19v2mlCIP16dzZbc1\n7rAxBU3Xm6xPwueTqGw4P81MNXKqPnKQulFtYFjyUE7VN45pCU9iMtPa7qbbldq6UxXUkSkXmpZy\nbzmGyMdbm7+rLW11TYt2Mh98PafPhVapjT7uRlI06zViCO7HDV2uC9PPxlNZ1viENtcZ/W5W0+cC\nDM1OZGvDXc5gt70ks5bxZ6Xzfa0Vm7vxokNkSW6ZDGMKDl89GcZAcpneRd/xEoHPjNfvi1pGPDgW\nLHTBJaxB2ptoQ8uJQSxJWfB4V+tqficvPI7BCYNQeWtw1gWXda7wovi5CYNOax1KSREcRkV+0hC2\nV1QD0FDfB71WhTvsLn/ws9KZ35/h2twDysrKyMjIICkpMLN704G6IqkS2kuWZd458B8+OPwpCRoL\n/zP2tohuC+G+3V3Gc//ejcfr52eT87js3Gwx35AABAYW/2rUbJ7Y/BQrS17h3vF3tnqns6m0hsqA\nL36wjy++O8EfVmzktqtGcPaQzit8IHStXbt2AVBSUhJ6TJIkXnjhhXiF1FzDV1WSLpG8pFxM6tim\nfGjrmlG6ZgCn3IExBG0lCe3lDEuqmvYCyE0YhNPtpTKsCEBrJ27hjwfnsYq34HxO/jbGbJzOCW1L\nf9t697/IBdHGj3QmCQlkGY/fi8/vjXk/bCpZl4RCUmBp6DrYnmNx8PS1vcdvbdi+GCzCAYE7X03b\nNLwrm1GvRqlQMiRxULte73RISMiyH5/sjzouT6vUkJeUi6OiruFvI1YU+Kc9bdTQsEqFAl9DFtGe\nO13R5g1L0Sfh1ms4XGdtiC320GKhkBSolWoStJbTnh8umCRZtJaIQiPhSZNGqQ5NTBzcT7oop2o7\nqbrjjjt48803WbRoEc899xy//OUv27VyWZZ5+OGH2bt3LxqNhkcffZTs7OzQ8hUrVrBmzRqSkwMV\ngf74xz8yaNCg9m+FcEbwy35W732TL49/Q7o+lbvG/IoUffNqUH6/zDtfHuSdLw+h1SiZV3Q2Y/LE\nCa8QaaAlixvOup4X97zOsm3Pct/4u6IWrgjSqJX88srh5GUl8OIH+/jb6zu48vwcrp0wuFk3EqHn\nWbVqVaeuz+VyMX/+fCorKzGZTCxevDh0MTGcLMvcfvvtXHrppcyYMaPNdYaf3AQqk8WmrZMqlRQ2\n54+i86qe5g5IYO+RatIS9WSnR/8MtZaEhBcW6M7EIWj4wKSIk28Im3y3jbOojpwrRtxtiLH7Xyxj\nWWKRmzgo6rokSYEfGZcvkOTqohSpaH09UrO7V/2TjZyoCnQDDZ7Ud5WmFS6btalCiUJS4Jf9pCd0\n4xTEx2TbAAAgAElEQVQrYWHEUj0wUZuAWtFwp1eSGD0kFZfHx/ehCZvbk6wG9mmFJIW6wLXn8BXL\nsS787mtnX9SWJInRaaM6tI6IO9Fh4TWt9qlVanB6naE2i1YN9HS1mVSFfwm9++677U6qPvroI9xu\nN6tXr2b79u0sWrSIZcuWhZaXlJTwl7/8hREjRrQzbOFM4/K5WVnyCtsrSsgyDeDOMbeGrniFq7W5\neObdXew+XE1qgo7/KTqbrDh2HRF6tvMHnEOls4r3Dn3M/21/nt8UzmlXWdyJoweQk2Fm2Vvf8e+v\nDrP/WC1zrh5JkllUlezJNm3axLPPPkt9fT2yLOP3+zl+/DiffPLJaa3vlVdeIT8/n7vuuov169ez\nbNkyHnzwwWbP+9vf/haacLirGHStH5a1Ch06hQG37GSgIafTXjPBqOHc4RmtLm96vtzayVWwZDh0\nXfeatrRUDTba1Xjo2MliePLc2l2C8BNlg9oQ88WfaJJ1zRP/pjQKFQ5PPW5foLx404IOHWE2qDkR\nGP+P19fW+x37vtB0tynITcHbwni8lirSDTHnUmo/ST9TbL0WOkN4HK2NVZMkBbLsb1baW5IC3dP0\nWhXYmq8vmux0E9+X1tIvxcDRU8G7SbH/fUsFVJoeP5WtTBmQYUyPy2e8qfAxk+Fb3vQ7S9mw33sb\n5tNLMGkor3Fg0Kqod3npLG1+20TOb9D+xtu8eTMTJkwAYPTo0ezcuTNieUlJCU8//TSzZs3imWee\naff6hTNDjauW/93yf2yvKCE/KY/fFs5pMaEqOVTF75/7lt2HqxmTl8rCW84RCZUQ1ZWDf8J5/cZx\n2HqU53a+hM/fvivkOf3M/P6WcxmXn8a+ozX84flv2XWoqouiFTrDQw89xKWXXorP5+PGG28kJyeH\nSy+99LTXt3nzZiZOnAjAxIkT+eqrr5o95/3330ehUHDRRRfFttLTPE9XKRWMHdr6SWF/7UBydPn0\nS45tjFbniDz+t3bidzrz/HQ1RUx3hU4/qdKowwfbt7L2sAXpMXZT7iyahvfE5WucuLkrtLcIRVMG\nVeDuUtPEw6hTt5gsh7fpQEugB1RhXiaXnDWaRGP3jZENnhsrFEpSW+h5A1CQOpy8pNzQ/Jv9kwNd\nMA0tVCJsT1KUmqDnvOEZJJkaP3ftGXMeamtJQq82oFKoQvtLkDLsDmH4mrPNmQy0ZMX8Wl0lvHuv\nJEnkDQj0CkhLjLyTb24ouZ/YUHxlcD8Lw3OSSU/q3LuaMV+yOJ0rOTabDbO58YtfpVLh9/tRNNxy\nvPLKK7nxxhsxmUzceeedFBcXc/HFF7f7dYSe66j1OP/c8Tw1rlou6H8ON5x1fbO5ERwuL2uLD/DJ\nlmMoFBI3XDKUKeOzxPgpISaSJHHjsCJqXXXsrNzN87te4RcjZrYyB0fLDDoVv75uFB9tOsZrn37P\nE6u3cc2EwUy9YFCHTxaEzqfT6Zg2bRqlpaVYLBYeeeQRrr/++pj+ds2aNaxcuTLisdTUVEymwEHX\naDRis9kilu/fv59169bxj3/8g6eeeiqm1+nIXqONUt3UpFOT2I1z9Om1KtIS9SSbAycqrX03p+iT\nqXRWUefq2rt57RHzBKenSZIkks06nG5f693/CD8x7d7vk2Blw+Cdqs48robvg5mprY/Vam3S4XCZ\npn4Y1YZWE5OmwtcV7AKpkCRM+u6daiXYzdWiMbd6F1Cj1EQkKzn9zAzMMLX4XrR3/5AkKSKRas++\nrFKoOCt5KFqlFrVC1WIBlcD0CY3R9TRNC9GkJuoxGzRoNZHfoan6ZMwaY+huokIhkWDU4HJ33l0q\niJJU7d+/n0suuQQIFK0I/hwsqfnxxx+3uXKTyYTdHjZTe1hCBXDzzTeHDmQXX3wxu3btippUpaV1\n59W5znMmxt3RmDcc+oZntryE2+fhxrOv4+phU5p9iXy76yT/t2Y7FbVOstJN3D2zkPyB0bs0tKUv\ntnW89KS4H5j8axZ9voytp3agUkvcff6vUCmbf8W1FfOsK0ZQOLIfj72wibc+P8jhMhv33jiuR0wy\n3ZPaOt60Wi01NTUMHjyY7du3c/7551Nf37w8cEuKioooKiqKeGzevHmhY5Xdbo+4GAjw1ltvcerU\nKW666SZKS0vRaDRkZma2eddKkjr2nlnMta0uMxs03b4/pKdbQj+rHH7KfIG7AU3jSEo5m+/KdpOd\nMIBUQ/QYu3o7DG4lFXIZRo2+2WuZ6wPbkJJsIs10+nFE24Z6tZF6ZeC1UlNNMbVLe9bfFo/WjlVR\ng8mgxanSB7bV3HltPjXdjMvjQ6dp/XSyWjLitTtbfA8amckm9rt4wXUCpKaYSDXG5/sxwWNA7ZFI\n0Le1bdGF9sVEE2kJ7VuPx+vHUhb4/mothlYfp+3XkmWZ0monsiyTnZnY46ovq0xZuE/Vk5OY2e52\nA/ArlVTYok+uHXM8bS18//33O7TywsJCPv30Uy6//HK2bdtGfn5+aJnNZmPq1Km899576HQ6vv76\n62YHupaUl/ecK2CxSkszn3FxdyRmn9/HG9+vo/jYl+iUOm4vmMXo1JFUhM25UlphZ23xD2zdX4FS\nIXHVBYOYekEOapWyQ23V19o6nnpi3LeNuJl/7ljBxtLt/PnTp/jVqNkRpYhjiTnFoGbhzeNZvm4X\nW/eVM2/Jp/z62lEMyYy92EBn64ltHU1Xnizfcsst3H333Tz55JMUFRXx7rvvMmrU6Q94LiwspLi4\nmIKCAoqLixk/fnzE8vnz54d+Xrp0KWlpaTF0A5Q69J6ZNAocLi+5AywcLrNRUdtYxtzv8cZ1f6hz\n27HWBeJpKY4sVQ6yHcrtbcfYXfv1AFUWeoWu2WsFt6FKqkdydF0cNTYHVlvgtaqV9cj22O+mdLSN\nqusdWOscKFxWrC4H1dSjcnb+tra1RqMvAclVS4omvdPe75paB1aHA7NFT1WVHbm+e+9QBdXVOXF4\nHEguNeXS6W9baF+U7Wjd7VuPX5aps7b+eezoPjQwxYBCAZWVtuhP7nYKcrVDULvVp7WNNXXOUNt1\nhjaTqo7OVj9lyhS+/PJLbrjhBgAWLVrEunXrcDgcTJ8+nXvuuYfZs2ej1Wo5//zzQ33ahTNXtbOG\n50pe5kDtIfoZM7i94KaIUteVtU7e/uIgX+48gSxDXlYCN112lhg7JXQKrVLD3LN/wTPfrWRn5R7+\nvvUZbj/7phbH8LXFpFfzP0Vns/6rw7z5+QEWv7SFGy4Zyo8LM0W31B7gpz/9KZdffjmSJLF27VoO\nHTrEsGHDTnt9M2fOZMGCBcyaNQuNRsMTTzwBBCrU5uTkMHny5Havs6O7ycCMsK7zyp61z3V3F7aO\nilYYoqs/0hETBLc9lL3TBbuDBecNi8f3l0apaTZhcUeFb8bpzPvVWYLtG8+iDV3dRb1pRc2epq05\n3KLp7M9D55WBaYEkSfzhD3+IeGzw4MGhn6+++mquvvrqrgxB6EY7yktYtfs16r0OxqWPZtawolD5\n1kMn6/hw41G+3X0Kn18mM9XI9RfnMiYvVZykCp1Ko1Qzp+BmXtzzOpvKtvGXjU9yx9m3kGUe0K71\nKCSJqRcMIneAhaffKeGlD/fxQ2ktN18+rFl/baH7fPrpp+Tl5ZGdnc1HH33EmjVrGD58OPn5+RHd\ny9tDp9Px97//vdnjt9xyS7PH7rrrrpjW2ZknOqom2xXvmltnWlIVTVdvT+RcVt3bdsEkLjjmpLe8\nd+Hb0dXl3NuSrk/joNtOmiGlU9bXO96dM0dnX7Dq2emncEbw+Dy8vu9tnv5uJR6/h5lnXc8vRs5C\nIav4uuQki17czB9XbOKrkjLSk/TceuVw/vDLcxk7NE0kVEKXUCvV3DJiJlflXka1q4Yntixjy6kd\np7WuEYOS+f0t5zAk08LXu8p4ZNUmyqpiG78jdK5nn32WpUuX4nK52LNnD/fddx+XXHIJ9fX1PPbY\nY/EOL8KgAZboT4qRsqfdqeol39uGhslaT3fuplhF3qnq3rYLJnGhpKp3vHURBUjiMSdaUIo+idHp\nBTGVt4/N6b1BwwcmMTwntiIfQiOzQcOITmy3Lr1TJfR+pbYTrCh5heP2k2QY0vnlyFl4bCZe/GAf\nX+8qw9FQ/78gN4Up52QxclByrzkgCz2bJElcPugSMgzpvLBrNc/ufJEf7D9wVfYV7T6JSrboWDCr\nkFc//p6Ptxzjjys38qsrRzA2v3vLI/d1b7/9Nq+++ip6vZ4lS5bw4x//mOnTpyPLMldccUW8w4uQ\nnmTotPEj6iYTn/aA6WF6hbOS8nD5XOhVXVuCO3yah+4+/kmh7n+BGHrLnarwpCrepfzVnTj31+nq\nCcWUzlQWY+ftP/HfE4Qzkl/28+nRL3jnh/fwyj7OTT+XZNsY/vnqUY5XBKrQJJo0/LgwhwsL+tMv\nuRtnOBeEMGPTC+hvzGDFrlf47OBXlJzcx80jbmBwQvsmT1UpFdz4k3xyB1hY+Z89PLn2O648P4fr\nJuT2uIpIvZUkSej1gRPgb775hlmzZoUe783amhBYOH1KhRKDouuPTan6FI7bTgDx6/4XzzFVXSE8\nqUrSJcYxEkFoJL6phXarcFSyavdrfF9zEL3CQL+6c9mwUYcsH0GllBg/LJ2LCvoxcnAyytMc4yAI\nnamfMZ37xt3Jxyc/4909H/LE5mVcOOBcrh7yU4zq9p1UnT+qH1npJp5a+x3//uowB47XMefqkZ16\ntUtomVKppK6ujvr6enbv3s2FF14IQGlpKSpV7z2cGXRqRgxK5miZDavDHe9whHbShA2kj3v3v14y\n6iNNn0Ktq45RGXl4zqzCqEIv1nuPQkKnk2WZz0u/5s3v/43b70Zl60/VvmFUebXkZJiZMLo/5w7P\n6PbJ9wQhFiqFip+Pvo5cfS6r973JF8e/YVv5Tq4Z8lPO6zeuXZMFZ6ebWHjLeJav28227yv4w4qN\nzL12FHlxLLveF9x+++1ce+21eL1eioqKSE9PZ/369fzv//4vd95552mv1+VyMX/+fCorKzGZTCxe\nvJikpMgxEsXFxSxbtgyAkSNHsnDhwg5tS3tZDJoeX4VLiE6KYTLizn29hiSuoc9oL7lRhUqhYljy\nUBJ1ZsqtvSer6iVvT58lkiohJqfqK1i163UO1B0Erxr34bPx1mZy4fAMflyYxeD+nTcoWxC60tCk\nXB4457d8cvRz1h/8kJf2rOHjIxu4KvcyRqeNirl7jEGn5q5pBbz39WHWbjjAYy9tYdrFQ/jJudnd\n3sWnr7j88ssZO3Ys1dXVoRLqRqORRx55hPPOO++01/vKK6+Qn5/PXXfdxfr161m2bBkPPvhgaLnd\nbmfJkiWsWrWKxMREnn32Waqrq5slXt0nvoOqgmNYErTiIkJ7yXL3VqpTNLkz1VvGVAlCTySSKqFN\nPr+PDw5tYP2hD/Djw1edhurEaKaOGcol47LEXSnhjKRUKJmSM4nxGWNYf/Ajvj65iX/tXMVAcxY/\nyZnM6LSREX32W6OQJK48fxC5/S088+4uXvv0e/YcqebWK4djNojugF0hIyODjIyM0O8XX3xxh9e5\nefNmbrvtNgAmTpwYuiMVtHXrVvLz81m8eDFHjx5l+vTpcUyo4k+lUDE2/ex23d3t6wYlDKTKWYNW\n2b0FBZpeJOotY6oEoScSSZXQqv1VB3l+x+vU+iuQPRpUJ8cw9awfMWlqJnqt2HWEM1+SLpEbhxdx\n6cCJrDv4AVtPfcfynatI06dwycCJnNtvXEyVpYYPSubhX57Lv94tYccPlTz8/EZ+NXUEw3P67ol3\nT7VmzRpWrlwZ8VhqaiomU2CCWKPRiM1mi1heXV3NN998wzvvvINOp+PGG29k7Nix5OS0r9hJZ+kJ\n1f9EQtU+qfoUUvWdM5dRezS9OCTuVPVwIuk9o4kzY6EZq9vGU++9zK66bYEHqrK4pP9PmDpzqJj0\nVOiVMozp3Drq55TZT/Hx0Q18c2Izq/e+yVvfv8e5/Qq5KPM8Mk3921xHglHDPTPGsP6rw7z1+UEe\nf2UrU8ZnM+3iXDRq8bnpKYqKiigqKop4bN68edjtgaqldrsds9kcsTwxMZGCggKSkwPzmYwfP57d\nu3fHLakShFiJpEoQuo9IqoQQj9/L2pKP+bxsA7LCg1xvZrR+Ejde+SPRzU/oEzKM6cwaVsSVg3/C\nF6Vf898TG9lQ+l82lP6XgeYszu1XyPiMMZg1phb/XiFJTL1gECMGJfOvdbv4cNNRdh6s5FdTR4hx\nhz1YYWEhxcXFFBQUUFxczPjx4yOWjxw5kv3791NTU4PJZGL79u3MmDEj6nrT0sxRn9Me5TY3XiQM\nOlWnrzteest2dKWOtlGFnEadyxZal0HdtfNydbfesA+Z6wPvSXKykTRz529Pb2ijM4Ekyz2hI0Hs\nOmsyxe6Ulmbu0XH7ZT+fHtjIuwffx6OwIXvVZMuF3HLOT+iffGZ9EHt6W7fkTIwZzsy42xuzz+9j\nZ+Vuvjz+Lbur9uGX/SgkBfmJQxibXsDotFGtJlguj481n/3Ax5uPIUnw47FZXDcx97TmHDpT2/pM\n4XQ6WbBgAeXl5Wg0Gp544glSUlJYsWIFOTk5TJ48mfXr17N8+XIkSeKKK67g1ltvjbrezn7P9h+r\nobLOiV6jYnReaqeuOx7OxP26u3VGG3l8HraX7wRgVOpwdCpdZ4TWI/SWfWjTya0AZFuyyDB07qTy\nvaWNulJnHa9EUtUNeuoO7fP7+PLYFt7Z/wEOqRbZL2G0D2X26Cu55Jz8HhlzND21rdtyJsYMZ2bc\nHYm5zm1lc9l2vj25hSPWY0CgK82QxEEUpI5gVMpwMgxpzQaC7z5czar393Kyqh6LUcOMH+fxoxEZ\n7Rowfqa2dV/XVUmVTqNijEiq+oTOaqPgSbtIqnqmamcNx2zHGZacj1rRuZ3IeksbdaXOOl6J7n99\nkN1Tz4aj3/DxoS9xUIcsS6hrc7h66KX8eFS+qA4kCC2waMxMzr6IydkXUemoYmv5d2w79R0/1Bzi\n+5qDvPn9v0nVJTMseSjDkvM5K2kIBrWB4TlJ/OGX5/L+t0d497+H+Ne7u/jg26NcNzGXgtxk8XkT\nYibGwwin66zkodS46npVQtWbJOkSSdIlxjsMoYNEUtVH+GU/+6sP8GXpJraW78CPF9mvQFmTwyXZ\nk7hy0nAxsaQgxChFn8ylAy/m0oEXU+e2UlK5l50Vu9hT9T1fHP+GL45/g4REpqk/eYmDGZI4mAsL\nB3Lu8HN58/ODfLOrjL+9vp2hWQlcdeEgRg4SyZUgCF3HrDG12lVZEITOIZKqXsztc7O/5iAllbvZ\ndHIHdm9goKrfqUdVnc+UIedz2cQ8UdFPEDrAojFzfv/xnN9/PD6/j8PWY+yp2se+6h84WHeEY7bj\nfHbsSyBwYpOTk80lg1M4eFDm+4NW/vpaFf2TzVwyLosLRvVDpxFfy0IUZ1SnfUEQhL5BHL17Eavb\nxuG6oxyqO8oPNQf5ofYQPtkHgOxR46vOwuwaxGUjxjDh0gHi5E0QOplSoSQ3IYfchByuGDwFj9/L\n4bqjHKg5xGHrUQ7XHWNn5e7Ak02gKwj8WO3W8tpRPWsO6kkzJjIoNZUhGamYtEb0Kh21ihTsNg9a\npQZNw39qhSqmCYqFXkTczBQEQeixxFn1GcQv+7F76qlx1VLlrKbKWcOp+gpO1p+izF5GrTtyIKJc\nb8Fbk4Jcl0p+ci6XjhvI6CGpKBTiyCwI3UGtUJGXOJi8xMGhx6xuG8dtJzluP8kJexkVjkpO1VdS\no6lBpoYKTlBRB5vqYlu/RhFIsrRKDTqVDp1Si06lw6DSYVQbMaj1mNRGzBoTJnWgC1CCxoxaKaZJ\nEARBEITO0qVJlSzLPPzww+zduxeNRsOjjz5KdnZ2aPknn3zCsmXLUKlUTJs2jenTp3dlOF3C5/dR\n73UE/vM4cPlcuH1u3D43HtmH1+9FX62izlqPHxm/348fGVmWkfHjl2V8sg+/7Mfn9+Hxe/D4vbj9\nHlxeFw6vE4fPid1tx+axI7fS70Mrm9A4+mGvMuG3J+C3J5CZlMQFI/tx3ogMki1icKog9ARmjYmz\nkvM4Kzkv4nGf34fVbWPPiTK2HTzGvhPlWF31SCovKD2o1TJGo4ROBxoNKFV+UPjwyh7cfg82j50K\nZxVevzemOAwqPQlaC4naBJK0CYF/dUkk6xJJ1iWSpk/tM+O8XC4X8+fPp7KyEpPJxOLFi0lKSop4\nznPPPce6detQKpXMmTOHSy+9NE7RCoIgCD1RlyZVH330EW63m9WrV7N9+3YWLVrEsmXLAPB6vSxe\nvJi1a9ei1WqZOXMml1xySWjG+p5qc9k2io99hdVjxeq24/A6uvT1FJICvUqHQWUgQZ2MwqfF79bi\ntGmoq1FQV61Gdhpx+FUoFRJnDUzk7OGpjB6SQkayoUtjEwSh8ygVShJ1CfxocAI/GpwPQEWNg91H\nqjlUZmPXwSpOHahvdllFo1aQYtGRYtSQYNJiMijR6fyoNH4UGg8o3fgkFx6cuKjH4bNj99qxeqzU\nuOo4YS9rMZ5rh1zBlJxJXbvRPcQrr7xCfn4+d911F+vXr2fZsmU8+OCDoeVWq5VVq1bx0UcfYbfb\nufbaa+OSVCWbtVTUOkhP6l2TtwqCIPQGXZpUbd68mQkTJgAwevRodu7cGVr2ww8/kJOTg8kUqEYz\nbtw4Nm7cyGWXXdbh162rd+N2+0AChSQhSRIKicC/CgmlovFfpUJq19XYA7WHOVB7CJPaSJI2gSxT\nf4xqAwaVAb060PUm2BVHpVCjkpQkJ5qwWV0oJAUSEpKkwO8Dr9eP2+vH65XweGScLj8uNzidfmx2\nGavVT43NS1WtiwqHp1ksCSYNBf3NDM1KIC8zgcH9LWjUouiEIPQWqYl6JiTqub5hnhGn28uxcjvH\nTtk4VeOgvMZBebWDKquLE5X1UdamAiwN/wUoFRJqjR+13oNK50KhdSJpHKB2sWmTn32bv0OjVjA0\nK5FJYzO7clPjavPmzdx2220ATJw4MXTxL0iv15OZmYndbqe+vh6FIj5j2ZItOsblp4tKrYIgCD1Q\nlyZVNpsNs7lxQi2VSoXf70ehUDRbZjQasVo7PjnZvqM1PPbSlnYVR1JIkUlWeOKlkBoSL0UgMVNI\n/UiSrkNCgQtwS1ANNE6hLOOXwe+X8cse/H43Mg7cHh8+nxxIonz+mGNTqxQkm7UM7m8hPUlPepKe\nrFQjmekmLAZNO7ZSEIQznU6jIi8zcBGlKa/PT53dTV29G7vDi93pwe7w4HD7cLq9ON0+nG4fbo8P\nt8ePy+PD7fXh8QQu7rjtPlw1ftzewPIKAMoB+KG0rtckVWvWrGHlypURj6WmpoYu8BmNRmw2W7O/\ny8jI4IorrkCWZW6//fZuibUlIqESBEHombo0qTKZTNjt9tDvwYQquCz8wGW327FYLM3W0VS0WY/T\n0sy8U5jd5nOE2HTWDNPd7UyM+0yMGc7MuM/EmCG2uPt3QxxnuqKiIoqKiiIemzdvXuhYZbfbIy74\nAWzYsIGKigo+/fRTZFnm1ltvpbCwkIKCgjZf60zd17qTaKPoRBu1TbRPdKKNukeXXvIqLCykuLgY\ngG3btpGfnx9aNmTIEA4fPkxdXR1ut5uNGzcyZsyYrgxHEARBEJoJP1YVFxczfvz4iOUWiwWdToda\nrUaj0WA2mzulZ4UgCILQe0iyLHfZNILh1f8AFi1aRElJCQ6Hg+nTp/PZZ5+xdOlSZFmmqKiImTNn\ndlUogiAIgtAip9PJggULKC8vR6PR8MQTT5CSksKKFSvIyclh8uTJPPnkk3z++ecoFArGjRvH/Pnz\n4x22IAiC0IN0aVIlCIIgCIIgCILQ24kRr4IgCIIgCIIgCB0gkipBEARBEARBEIQOEEmVIAiCIAiC\nIAhCB3RpSfWOcrlczJ8/n8rKSkwmE4sXLyYpKSniOc899xzr1q1DqVQyZ86cuMxyHy6WmIuLi0OT\nS44cOZKFCxfGI9QIscQNhOZoufTSS5kxY0YcIm0US8wrVqxg/fr1SJLExIkTufPOO+MUbWThFo1G\nw6OPPkp2dmP5/08++YRly5ahUqmYNm0a06dPj1usQdFiXrduHS+88AIqlYr8/Hwefvjh+AUbJlrc\nQQsXLiQxMZF77rknDlFGihbzjh07eOyxx4DAvEqPP/44Gk3856qLFvc777zDihUrUCqVXH/99b2+\nIFGs+15f4PV6eeCBBygtLcXj8XDHHXeQl5fH7373OxQKBUOHDuX3v/89AK+99hqvvvoqarWaO+64\ng0mTJsU3+G5UWVnJtGnTeP7551EqlaJ9mnjmmWf45JNP8Hg8zJo1i3POOUe0URiv18uCBQsoLS1F\npVLxpz/9SexHYbZv386SJUtYtWoVR44cibldYj0vjiD3YM8//7z85JNPyrIsy//+97/lRx55JGJ5\nXV2dPGnSJNnr9cq1tbXy5MmT4xFmhGgx22w2eerUqXJ1dbUsy7K8fPlyuaqqqtvjbCpa3EF//etf\n5RkzZsirV6/uzvBaFC3mI0eOyNOmTQv9fsMNN8h79+7t1hjDffDBB/Lvfvc7WZZledu2bfLcuXND\nyzwejzxlyhTZarXKbrdbnjZtmlxZWRmvUEPaitnpdMpTpkyRXS6XLMuyfM8998iffPJJXOJsqq24\ng1555RV5xowZ8hNPPNHd4bUoWszXXHONfOTIEVmWZfn111+XDx482N0htiha3BdeeKFcV1cnu91u\necqUKXJdXV08wuw2sex7fcUbb7wh//nPf5ZlWZZra2vlSZMmyXfccYe8ceNGWZZleeHChfKHH34o\nl5eXy1OnTpU9Ho9stVrlqVOnym63O56hdxuPxyPfeeed8mWXXSYfOHBAtE8T33zzjXzHHXfIsizL\ndrtdfvLJJ0UbNfHRRx/Jv/3tb2VZluUvv/xSnjdvnmijBv/617/kqVOnyjNmzJBlWW5Xu8R6Xqbg\nv+MAACAASURBVByuR3f/27x5MxMnTgRg4sSJfPXVVxHL9Xo9mZmZ2O126uvrQxMLx1O0mLdu3Up+\nfj6LFy/mxhtvJCUlJXrm2w2ixQ3w/vvvo1AouOiii7o7vBZFi3nAgAEsX7489LvX60Wr1XZrjOE2\nb97MhAkTABg9ejQ7d+4MLfvhhx/IycnBZDKhVqsZN24cGzdujFeoIW3FrNFoWL16dehuSbzbN1xb\ncUPgc/jdd99xww03xCO8FrUV88GDB0lMTOT5559n9uzZ1NbWMmjQoDhFGilaWw8bNoza2lpcLhcA\nkiR1e4zdKVp79CU//elP+c1vfgOAz+dDqVSya9eu0DxgEydO5L///S87duxg3LhxqFQqTCYTgwYN\nCk3F0ts99thjzJw5k/T0dGRZFu3TxBdffEF+fj6//vWvmTt3LpMmTRJt1MSgQYPw+XzIsozVakWl\nUok2apCTk8NTTz0V+r2kpCSmdtmzZ09M58VN9Zjuf2vWrGHlypURj6WmpmIymQAwGo3YbLZmf5eR\nkcEVV1wR6pbWnU4n5urqar755hveeecddDodN954I2PHjiUnJ6dHx71//37WrVvHP/7xj4gdtLuc\nTsxKpZLExEQgcOAaMWJEt7ZzUzabDbO5cVZzlUqF3+9HoVA0W2Y0GnvE5KJtxSxJEsnJyQCsWrUK\nh8PBBRdcEK9QI7QVd3l5OUuXLmXZsmWsX78+jlFGaivm6upqtm3bxu9//3uys7OZM2cOo0aN4rzz\nzotjxAFtxQ0wdOhQpk2bhsFgYMqUKaHPbG8VrT36Er1eDwTa5De/+Q133313qAsrNH5v2+32iDYz\nGAw94vuvq61du5aUlBQuvPBC/vnPfwLg9/tDy/t6+0DgnOn48eM8/fTTHD16lLlz54o2asJoNHLs\n2DEuv/xyampq+Oc//8mmTZsilvfVNpoyZQqlpaWh3+WwWaTaapfg49FykKZ6TFJVVFREUVFRxGPz\n5s3DbrcDNNtogA0bNlBRUcGnn36KLMvceuutFBYWUlBQ0GNjTkxMpKCgIHQyOn78eHbv3t2tJ/un\nE/dbb73FqVOnuOmmmygtLUWj0ZCZmdltd61OJ2YAt9vN/fffj9lsjvt4H5PJFIoXiDjRMplMER9Y\nu92OxWLp9hibaitmCHxB/eUvf+Hw4cMsXbo0HiG2qK24//Of/1BTU8Ntt91GeXk5LpeL3Nxcrr32\n2niFC7Qdc2JiIgMHDmTw4MEATJgwgZ07d/aIpKqtuPfu3ctnn33GJ598gsFg4L777uP999/nsssu\ni1e4XS7aZ6avOXHiBHfddRc///nPufLKK3n88cdDy4Lfcz31+6+rrV27FkmS+PLLL9m7dy8LFiyg\nuro6tLyvtw8EvvuGDBmCSqVi8ODBaLVaysrKQstFGwXGjk+YMIG7776bsrIyZs+ejcfjCS0XbdQo\n/Ls4WruEf5e3do7ZbP2dH3LnKSwspLi4GAgUdwjesguyWCzodDrUajUajQaz2Rz3rDtazCNHjmT/\n/v3U1NTg9XrZvn07eXl58Qg1QrS458+fz6uvvsqqVau4/vrr+cUvfhH3boDRYgaYO3cuw4cP5+GH\nH457t6PweLdt20Z+fn5o2ZAhQzh8+DB1dXW43W42btzImDFj4hVqSFsxA/y///f/8Hg8LFu2rEcU\nTQhqK+7Zs2fzxhtv8MILL3D77bczderUuCdU0HbM2dnZ1NfXc/ToUSDQxawnfG9A23GbzWb0ej0a\njSZ0Z7Ouri5eoXaLaJ+ZvqSiooJbb72V+fPnc9111wEwfPjwUNfmDRs2MG7cOAoKCti8eTNutxur\n1cqBAwcYOnRoPEPvFi+++CKrVq1i1apVDBs2jL/85S9MmDBBtE+YcePG8fnnnwNQVlaGw+HgRz/6\nEd9++y0g2gggISEhdEfFbDbj9XoZMWKEaKMWjBgxIubP19ixY6OeYzYlyeH3wnoYp9PJggULKC8v\nR6PR8MQTT5CSksKKFSvIyclh8uTJPPnkk3z++ecoFArGjRvH/Pnze3zM69evZ/ny5UiSxBVXXMGt\nt94a15hjjTto6dKlpKWlxb36X7SYfT4f9957L6NHj0aWZSRJCv0eD3JYVTCARYsWUVJSgsPhYPr0\n6Xz22WcsXboUWZYpKirqEVXS2op55MiRFBUVMW7cOCAwVuamm26KewVOiN7WQW+++SYHDx7scdX/\noHnM33zzDUuWLAFg7NixPPDAA/EMNyRa3KtXr+aNN95Ao9EwcOBA/vSnP6FS9ZhOEp2upfYI3mHs\nax599FHee+89cnNzQ9/BDz74II888ggej4chQ4bwyCOPIEkSr7/+Oq+++iqyLDN37twe8T3SnW66\n6Sb+8Ic/IElS6GKVaJ+AJUuW8PXXXyPLMvfeey+ZmZk89NBDoo0a1NfX88ADD1BeXo7X6+Xmm29m\n5MiRoo0alJaWcu+997J69WoOHToU8+ertXPMtvTopEoQBEEQBEEQBKGn69Hd/wRBEARBEARBEHo6\nkVQJgiAIgiAIgiB0gEiqBEEQBEEQBEEQOkAkVYIgCIIgCIIgCB0gkipBEARBEARBEIQOEEmVIAiC\nIAiCIAhCB4ikShAEQRAEQRAEoQNEUiUIgiAIgiAIgtABIqkSBEEQBEEQBEHoAJFUCYIgCIIgCIIg\ndIBIqgRBEARBEARBEDpAJFWCIAiCIAiCIAgdIJIqQRAEQRAEQRCEDhBJlSAIgiAIgiAIQgeIpEoQ\nBEEQBEEQBKEDRFIlCIIgCIIgCILQASKpEgRBEARBEARB6ACRVAlCnJSWljJ27NhWfxcEQRCEeBPH\nKkGIjUiqBCGOJElq83dBEARBiDdxrBKE6ERSJQg9hCzL8Q5BEARBENokjlWC0DKRVAlCHLnd7tDP\nLpcrjpEIgiAIQsvEsUoQohNJlSDEkdfr5YsvvgDg448/jnM0giAIgtCcOFYJQnQiqRKEONJoNKxY\nsYKpU6eyc+dO0U9dEARB6HHEsUoQolN19Qts376dJUuWsGrVqojHd+zYwWOPPQZAamoqjz/+OBqN\npqvDEYQeRalUsnz58niHIQh9mt/vZ9GiRZSUlOB2u5k3bx4XX3xxxHNee+01Xn31VdRqNXfccQeT\nJk2KT7CCEAfiWCUI0XVpUrV8+XLefvttjEZjs2ULFy7kySefJDs7mzVr1nD8+HEGDRrUleEIQo8j\nrvYJQvy9/fbb+Hw+Xn75ZcrKynj//fcjlldUVLBq1SrefPNNnE4nM2fO5MILL0StVscpYkHoXuJY\nJQjRdWn3v5ycHJ566qlmjx88eJDExESef/55Zs+eTW1trUiohD4nMzOTLVu2xDsMQejzvvjiC9LT\n05kzZw4LFy5k8uTJEct37NjBuHHjUKlUmEwmBg0axN69e+MUrSB0L3GsEoTYdOmdqilTplBaWtrs\n8erqarZt28bvf/97srOzmTNnDqNGjeK8887rynAEQRCEPm7NmjWsXLky4rHk5GS0Wi1PP/00Gzdu\n5P777+fFF18MLbfZbJjN5tDvBoMBq9XabTELgiAIPV+Xj6lqSWJiIgMHDmTw4MEATJgwgZ07d4qk\nShAEQehSRUVFFBUVRTx2zz33hO5OnXPOORw6dChiuclkwmazhX632+1YLJYuj1UQBEE4c3RL9b+m\nE8VlZ2dTX1/P0aNHAdi8eTN5eXntXo8gCIIgdNS4ceMoLi4GYM+ePQwYMCBi+dlnn83mzZtxu91Y\nrVYOHDjA0KFD21ynOF4JgiD0Ld1ypyo4wHHdunU4HA6mT5/Oo48+yj333APA2LFjm1Vaam095eV9\nq8tFWppZbHMv19e2F8Q29wVpaeboT+ohpk+fzsMPP8yMGTMA+OMf/wjAihUryMnJYfLkycyePZtZ\ns2YhyzL33HNP1Gq1ffF41V597TNxOkQbtU20T3SijaLrrOOVJJ9hl9P62o7RFz8MfW2b+9r2gtjm\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Ts7vRVpjT3xan1FP1xje+kfHx8YbvBUGgs7MTgDvvvBNN03jta197KrvSQgsvK2i6xf2/\nOIzaM83h7K8oThTpT/by7m1v5dyus1/QvnRkYnzkLefyl995hq8+uJvP3XQpH7/8g8wXlnh6djf3\nH3yIHdve9oL2qYUXN5LJJLfccgu33HLL6e7KslgoaHSmvX3L49k4qJjebqtpRXddw4noC4UKtpNh\nz4hnTKmyhGHZJGISQz2npkbM1EKZo1N5hvsyDHSlVr8ghDO1ltNKz2Wl8D//mGZESWbFsIirx2dW\nmZbDE/tnaUuqnLOxM3LMdd01bf0/VzWqO9vikf7YjhsxNKuNLtuO7ThIosh0eZb5fAVFmEbTN5BO\nNO6/u667Jg+A67ocHM/Rno7R016r3bTr0BypuMKWoagoxWpkQxDAdAzm9SKW420Kli2PxFhNQiUX\n8hVSCYWYsnL+0pPTz6BKKhf0vGLVMYVxOHeUxcoSr+q9kKmyR35Ldk39LrzGwiIOlmOjrtCl/YsH\nyesFxouTXNx3YcNx13WZXvDGXdYbxSEs2zml9arqiYnhmACMzRSYnxpD6ZhHFKAj3o4inhjVKBol\nYnKs6fVOM1IVev5WtT8VqxJ9BrbJc3PPH1ftwOPBaROqcF2Xr3zlK4yMjPC3f/u3a77uTKlH8EKi\nNeaXPk72eP/xoV1oPU8i9x5Dd2Ted/7beev2NyKfxMTY48EbejKMzpb454cP8u2fHeL//sDFfPqq\n/8h//cmf8/DYrzhnYDNXbnrNaenbC4mX27o+Vdi+fXuDQdfT08MvfvGL09SjRoSN2xPJn7AdN2K4\n+nkBtmszoY+xWauKzrgWOaNIQkyvmgekWRqPTx1kU9tGuhIdx9WfxYKXG7KQ14+bVJ0IXNelbGmk\nlBMPY1rN82DY5rLH1rrTH77DyFSBszc0zqvt2NiuHSlRMTlfYj5XYeNAFoBc2Wi4rpnhuBIcxyW8\nBGzHbVh7y7Xph5duHWwLjFgHe9l8lJJVJiUnVyVWpuUwn68wn68EpMp1Xcq6RVm3GkjVchjJH2N+\n0cTRU8yaE4hYzGkZBujAY54u9d6LcsVi/7ElJFHk/K1tiIKIukJ4oGE3PoPVsKB5YXeO6wTGezhv\nLTz/Ya/Tnvm9TQ17/53P64XqvxvX4cximamFMkvGIhYW7XJXwznWKuGrx4ORqQKTCyUuPqsHRZYo\nGiX21nmAHMfGcV0KmoEhLtLhOKiSSMksrarmWDbLiIIYCcE1bJO9C/uRRZmLes9vuKbZux0O/wsT\n7MNLRxvO9WtxnmysiVT90R/9Ee9617u45pprUJTjj1dtNvjPfOYzxONx/v7v//642jpT6hG8UDiT\najC8UHi5jflkj/fQzBQ/mL0LubfAUGqAPzz/D+hL9rA4X1794lOIN12yjqf3z/CLp8Y5b0s3l2zt\n4g/PvYE/f/z/49ad3yRpZ9nUtuG09vFU4uW4rk8V9u7dG3w2TZMf//jHPP3006fsfieCcG6LuYLx\nvhxmljT2jS6xoS9DIibhO6qKdg7dqXAwdxgYZEIfwcakXx0GViY7B2bHSbdbHJPGj5tU+fcvaAbl\nikUyfjx7sqsbeI7rMKct0BXvQBIlckaeg4uH6Up0sqlteG13qfOerEZmDWd5I9q0ViJVtXbDIY3L\ncdrHJ3dhOjavW39J8N3ItPdboBvLC1HUe6oqVgVBEIktUz/QdtyIN/DIZJ6u3rWRw9GZIgALBZ1k\nu7f55hmpzQe1d34/nYkOzHwWy4ZX1HnZloNP3pZDvb04PltkJDeBJiwys6SxKbEd0zWIuSLHChNs\nMYcQBCHIZwrDqoYu2o7D7rk9AAGRMWwTURCQRZljhYngmoqlE5ePPx3Fdu1g7sOkyjf0uxKdzGsL\nGJbDyHQBURAYSE8xmIoW6nVxVxUTOTzp5bfNmp5gSDNStfvIPJdt7wtk9deKolmibJYjNZ8mF7w1\nXq5YtKUlSlZzW8K0HFxcby4cF6TaxoXlWBwrTtAZ7yCrZjAdC1nw1tme+X0AEZLpC3xYjoVhmxEy\n7LpuQ/6Ud26YVK0s7X44N8Krei9Y8ZwTwZp8gzfffDO//OUvedOb3sQXvvAFdu3adVw3LxZG/gAA\nIABJREFU8X/kHnroIe6991727NnD/fffz759+7jhhhu48cYb+fGPf3z8vW+hhRYimCrN8P/t+l8I\nyQJbE+fzp5d8/ITrXpxsyJLIx95+Hqm4zNe++ywjUwV6k918+LwPYLsO//jcNyNqQi20sBYoisK1\n117Lo48+erq7EkHEuM2NnFAbi8UKuw7PMZurBMZRbefaa99yPaPFdq1VZZVdPKP2RBAmK/tGF5c9\nb/fc8+xdONB4b9elZBWXDbkbL04xmh9jtOClDOhVr8G8trY8mUNLR3li+ullhQGa3deXWm6GlXb6\nw+QpH/Iw5Up64NEL4/BUjrGZYtMNZjPkEZteLPP43pmA0IVJYdkss3vueZ5doZaZ47iRNVAxLBbK\nHlnakPWKY8elGmE4PJHn0T1TlCtWwJ1EQeDoVL7pWOuxoC2yd/4whSZeNh/1Qx6rkrdlzw+N2XFc\nxqqkygiRXAExmMsnJ3YHktr1z3gl1bdds7t5euZZoCa+AbBv8eByl6wIy7FDY63VB1uq5ABIK15Y\n7lyulvt2ZPEY83V5YKVV/gY2C6G1XQvNbsxXnFlaea6bYe/8fkbzx4KNoPDmkB+CLC5DHUzb8667\nOIHXfDQ/RtEoMV2eZa48z3hxEs3SeGbmWUYLx4L33LuXw1y+yK7Z55gLvfeFUMHkilVhrDDOwcXD\nwXfndnkiRX7IHzSKVtTDdV0e2zPN6PTJ3ehc01bTpZdeyqWXXkqlUuGHP/whf/zHf0w6nWbHjh28\n//3vDwQnmmFoaIh77rkHgLe85S3B93v27Pktu95CCy2EMV2a4S8e/3tMsUx66Tz++MoPnHEKYZ3Z\nOH/01nP5q3t38dXv7uazN13KOZ1n8Xsbr+YHR3/MN/fex0fOu+GMLKjZwpmD7373u8Fn13U5cODA\nCUVRnEqsJeTPdV0s2wlCapq24cLY0hQpKRNpN+zEqN/ZzpUMKrpFrmQw3O9dZ7s2RTuH5AqciEZX\n2EA1VwiNq0+sn1ksY9oOOWuexVKe9iKsa6Jk51/n1/VZznBrBsd1WKx4RM/LVRGZWiijqrVncGjp\nKNs6NkeuW8lTtVL430phhYfGc1yyvRfwhETCP2W242BaLntHFslZC6hCjKNTteNHqh6IXFGnuz0R\nMaBntfnIfUzLQRKFiCeiUDZQQ7lDhqNT0XKkEwoZNY0kyhHiMbPkGfB7RxeD56tbNrmy9ywEhFVD\nKMt2salB76OeBKz20x4+/+B4rjYWs2Yki4i4NBrNtutgOzZLeo6OeDthJ43L2la9eZwhgLbrVnPX\namIj/t+vY8WJIKTQD/0M/23TDZunj46TTAnkKxrd2Tj7Fg6smO/TTDp9Qh/Bck0GY8OoQhxBEDAd\nk4ePPMHl0kZ6ehrD51aDHyY6m6uVPnFDxLsZXEfAxcVyTRZyFqlej0iOFo4FoX0ls0ypWk7FL97r\n4+DkAgfnJohlKhjJ2nPwC6lPl2eDOl9h+F6snJ4P1Beb5deFoRs2Ii4T86UVi3sfL9bsv3/sscd4\n8MEHeeSRR3j961/Pm9/8Zh555BE+9rGP8Y1vfOOkdaiFFlo4fuT0PH/7zDfQ7DLGkXP5w9992xlH\nqHxcsKWb667exr0/OcBtP3iej73jPN686RoOLh3m6dnd/GriUa4YeunnV7Vw4njsscci/+7o6OAv\n//IvT7i9W2+9lV/+8pdeLkM+z9zcHL/61a8i53znO9/h29/+Noqi8NGPfpQrr7xyxTabeUYc140Y\nJEenCkwvljlrXTsVsXHH1HArWI5J3lhkkQXSDDW9l0eTau0+P9Lo3Zk3q7vxa9M+aEDOXGTeXKJL\n6W/wPqwkWuCHKlWcMipQMNe2e+6yctiaaZtYrsVCZYnJ4lTkmG7YHJ3KY7km8WpkVE7PkdMLZNRU\nIHlthna266WwVwr/W4lnhMUB9tQ9B8ux2TeaRzNNFswZADYltlfbrM2h/1+3Ts1ssagjiSLlismu\nw/N0pGMMdtdCPg9P5unvFTmi7WUotgnD1XGtWp6PKHhG78RcKeJdMiybuOKZg4Zh13KDqmF15jKh\nVHZ1IhatOWBLw/F82Yh4pjxitPLqm1wokhLbSMUVFgo1gu57Prx5gmZhiZZjMVYcZ648T8XWaZe7\ng2NLRZ2O9MlXmT46WcBxXXqS00GP/BFqZq3/quTNb/j9n1nSkAWTJU3EdA3SCaWhlAJATi+QVdPB\n86iH762e0EfIyh10KX0YrndvP+frhBG6n0/omnnLHABX8Priel/0xdYxrR+jbJZrUSiuiyQ02iaG\n5bBn9jAJKV0V6altkvnrcXYZNUFJqM3ZnLZAb7K7qaeqJ9kdtGE5NoJjoIgntxj6mkjVVVddxbp1\n63j3u9/NZz/7WeJxj3Fedtll7Nix46R2qIUWWjg+GLbB3z/zjyxUFjGPbeOijos5a3376e7WivjA\nm7bzzL4ZHt83y8NPjfOGV63jple8jy8+9v9y/8F/ZXvHWfQkG+PEW2gB4Mtf/vJJbe/mm2/m5ptv\nBuCjH/0o//k//+fI8bm5Oe68804eeOABKpUK73vf+/id3/mdFb1jXhhMzcByHJff7J1GQCCbUmlL\nqRyem0ZzSojjAn0DjUb8pD5Kh9yNAJiuQdkuBp4qxxHwTQnPuPVECXLFaPhZzRCqGsYCJ+QJntIn\nyFsGGakdVYwmlO+a3c1geoDBdP/y81H9b65oMCdpdLclIsdrfaoZzyvhcG4kEhYUhl71atS3cWDx\nIBvbNtCd8H5bwsah67o4VSInCmLEU1U2NUYKY2zKDhOXY4FhO2tMIAkynUovmwayTM6XVyzCatlO\ntd3oOXPGJCWnwLrYZiRBDrw5YW+n5VjM5TxD+ajkeXAWizqLdc/78NIoADlrgaSUxg68CyKiIGI7\nDqNz0XnrSMcCBTndskM1h7wwu2dmnsO0HZQ6NTm/lpeL07BhALDn6AIVp8ysMUFaauPwnE3ZtoDm\n5KZi2swvltDy87z63H5EQQg9I++/C9YMhmOgNDHMi0Yx8HhOFqdwqnl/rusyl6tgWg6PL81w3uZa\n/tfjU0817cta4fdvsZKnpJmhnkJCiQdrVBa934r6V8/BQfTDBatrp95rfWDxYPB+rSagkrcW6VL6\naEY6jwf+u1Mv0e/3uZ5AjkwVEN0opVBJIYkydh0pf2piP6IokA7lZfo5hi5QKegkYjKpmHfcV/Vr\nULIE+lK9CIJANpYlr+cpmSWgu2lOVVpJM8tcMBbDKZ0eUnX77beTSqXo6uqiUqkwMjLC8PAwkiTx\nwAMPnNQOtdBCC8eHe/c/yLHiBNLSMMbUFnb8UeOO4ZkGSRK5+W2v4PP/tJN7fnKALYNtDPe38Z6z\n3sFte+7mjue/zSdf9dHIznELLbzhDW9YkRD85Cc/+a3a/9GPfkRbWxuveU3UU7pr1y4uvvhiZFkm\nnU6zceNG9u3bx3nnnbdsWy5eQVm9auRVDKv6vUuupJMr6cwYXv5Qu9zFoYki2Sa5/i4ui4UKqbjM\nklELfbFtFylynpcH9Pwy+U5C9V2yHZdiRcdyLORVpI59743pWIHxpDuVCKnyjcaJ4iTdiegAloqN\n+UWT82Uo5gJSZdgmi3qjcEHYJFysLJFSUpFk9eUIFXglJbw2XEamC6zvTQcG/0Jlkc54hzcu20Yz\nLBKqzO755zFtE0VSSMgJFnUBCU958EhuBM3SOFacYGv7poB0FG3PC9epeIadJAoreriemd2N43Qh\nEjXkCrZHksp2kYzcHqzxetIXjM80ABHd0SjaOTrlvuCahbzngZLiGrYRD9oYmymS1w2WE78LFzt2\nXC+0UHAFHBeOTuUwbYctQ22RoEzfwBcQlo2vmzUmsVyLJWuekUKJhUIl8M41Q3icgiDgVsUmfK6a\nt7z1LdF8IGFjerI0CfQGzytXMuhMOMwuLZ9XmFCSgRDCau9HpN+4wXy4TVQAJUFEEMQgXFMVYwjV\nZ+j/nfPH+OvRZ8kVDdb3pZGqz3V0fhZBzwTk41RgsdL4HkaKSQdEy+HoVAHXcdk86KlXWraDLHjH\nOzNxFgoVKrqNI4Q2JwyLqbly0M62qvJjW6wN8N4Bv8ZXSTMDUjVVmmYoPdDU+xSr5ghubhvm6Zln\nsRwb07GaerXCmxSrKaWeKNa0Yn72s5/xwAMP8MADDzA/P89HP/pRbrrpJt773veekk610EILa8Nv\npp7k3yd3kqGbmQNn8+bLh1+QSuonA53ZOB95y7n81b3P8NXv7uZzH7qUS/ou4pnZ3Tw1+ywPj/2K\nqze8/nR3s4UzCHfeeedv3cZ9993H7bffHvnuy1/+Mueddx633npr0zDCYrFIJlOLu08mkxQKqxs3\nAgJxOY7pWCsKH7gu6FbzEKu87RmRpUr0eGMInhMkwTfvS80cHp8voMjPrpi7sXdygjlrglf0bCUm\n1bwzTl1YniAIlHSLmCKxKySi4LgOe5cheLZrsXviKN35fqYqB7AcC8f1dvHdkOHm49DSEboSXYE6\nqGGbVEwb03LIJOqNa5e5qhiHi4NhOeTLJu0pj8jk9QKHcyNszGxgz8gsFUdjU38G8LwMpm1i2ibj\n5QIbYmdV26m27PpGc/SOjusgAJIoYDtO4KWsR8WwyRtT9Crrm85LLV/Ojfzbm7OaQWlYFgIqk/oo\nLi6qkCAjewaqIHgERxJF5s1psqqK48L0gsa0UcFyDTbEox5Fx40amS6e18l1vb6YAVlwI66WGhHz\nPEpi09C+WrvhcL7lEB6zJ5Tu3buerDouZKUuXJYXdqj5uBpelmWvUUWFA0uHyet5Luo9v4FYmY7F\nkdxRTMdCMmo14VzHDd3Pv433qSfZjSiIuK4TIVr+5/rn7nski2WTtuq6nVgoQ6kQnLfS5lJXNk5x\nMR/py3KY1xbIG0U2ZtczXpysjQcXzapwtHAU121DFhRcp/ZuNvOYSYKM5ZrEFJFOpRfbcZFkEcPx\n3tWZRS0gVD5002HJsOhW+pkza2G8lu0wtaCRTsikEwqL+lJT77W/WSIJEk61b74cfT0UMRRSeIpI\n1Zq2gb/zne/wzW9+E/CEJ+6//37uuuuuNd3gmWee4YYbbmj4/qc//Sk7duzg+uuv59577z2OLrfQ\nQgsA89oi9+y7H1VUWdh9Lh3pBG957fDp7tZx4YItXVx7+QZmljS+9eP9CILA9We/i7SS4nuH/zdz\ndcnZLby8MTQ0xNDQED09PezZs4edO3eyc+dOHn30Ue677741tbFjxw6+973vRf5/3nnncejQIdra\n2li/vtHgTafTFIu13JBSqUQ2m131XqVq/RXbsVjQvF1gzS4xY0zUGQiN9YR8LKeWV5/XUHIKmI7B\nuH6EitNIrsIGr5/389z8Xp5f2N9wj3LF5PmpY4zNFDlWnGC8OBncz3YtZoxxCoY3H6WKzcRcKSIt\nDrBYyZGzwjlFtRHmrAWenTzK7qmDlA0D23U5NJFjemF5UuiF9XgwbC9XZ2qhjFVnHBU1M8gX8u84\nu+S1a1gOi0WdpcoSu6YPBvNUbx+atoPtNM572PgNPz/LNb0QTSoYTsWTNm9itM0saVRsPZSzFD3u\nVIlTTXK81kY4r8nzVNX641A7Jlb9l35Krev4pE9AQGwaQmXZjpcXJWtBu/7zDi+zola7j2E7Qeih\nwPI8ZTXBljltgZl8gbJuRcioUyUOy10vIGKV0isKXwhuNKQ0dPGycHHI6x4h8eXAHddhojiFYZss\naAvk9QKaqfHURE0pcHpRC27j4lCuWEHfe5so8IqhN7IW0usGHmWIKlA6IVI9YRxhQj+67Bh0u0K2\nutlgOw7lyvIlHY7kRpjX5oPiveGZOJobJWfkWTRnq31wq/2y68700KcO0aMM8Kq+C2mTO7Edh654\nN4cm8ozOFEkR9WTnyyajMwVmF3VEQUSRa2MvVSwKmsHkQhnDcqhYelMi6RPTifkyh8fzzJVyHM03\nqq1ubNtAW6y2MealfTmrhhkfL9bkqTJNM6Lwt1aVpa9//es8+OCDpFLR2hmWZfFnf/Zn3H///cRi\nMd73vvdx9dVX09m5tjoHLbTwcofrutyz/35026C/+Bpy5STvfftW4uppq+d9wnjn6zezZ2SRR56d\n4sIt3VyyvZcd297GbXvu5u699/Pxiz7SUgNsIYKPf/zjaJrG6Ogol1xyCTt37uSiiy76rdr893//\nd6644oqmxy644AL+6q/+CsMw0HWdw4cPs23btlXbTKfjdCRTSLrLVH6CbGYTU4XDIIGcsBnubGNm\nUSOTiuMYKpksJFYgFmGkknFUSSJRDSVzMcgL00iiiyHl6U16f0/bs3GQJMpyHKuqupVOKGSyfk6T\nQ6ZdJanWcpyWCjrJpIpgW6hJEQeDZFLFRsCkhIvLs/MH+L1zX03KjJFIeH2otQlz7jSavMRguh9J\nkMiJKnLcQnAFUkoMw1ApWnnaYikkSSSRULGAtrYEPT0ZykqOglhrrz2eQUrZzJbn6WxvD+6ZSsUC\nxTvdsBmdL5PNeNfJlk1CrPXt+aMeyetWZUyrErSRzsQiv52Lee9YJu0pqWXbDEomtMW9vikFnfRc\nkYTgXZ9MyHR1p9mT38uCqJHObiWuqmQnvB3zRF24H6pORzxBeyZG1kgEx5OqSjbm9T3bnqSNBHPl\nCplsAsdxg/4m4ypZpXZdXJHIxr3rcpL33NqyCUqGQyKhkErFkewkhXIMwbZIp2NVhTSTolUgnkhR\nqeQox8q89eIr+Lfn50BSWVgy6ehMBfctVCzW9XubCcemC4iyREKWSEpxurvTqIpEsWyQiMkcGs+R\nzSRICGpD2Jb/fDS7zKw9zb5jnkdzXW8ax/DmoK0tSVtbHEknmOcwklKcgb4sc8xF1l0YjiXhugks\nxwzayGYSdHQmKRvNr9EtncWyyUB3iu7uNCk1yejSOAVtCQGLZCZBRvCUGROJkDIeIKsiMpBSVJS4\nQjYRpyIn6O3OEFfibGKIin0MzXLYmNzEnDENVQ+1gEAiqbKht4+xeW8DIRZ6T+UFjXhSRBHVKm12\nGtdVFfG0hOomSRgqRd1h555pLtjaTUc23nBuplxdbx0x2pwEiun9re3sSnG0JGA4kE6qZBPeM+np\nybDzWPjdieO6kEhodGQydJBhaKCdY/MVMpk42WQbF/Z60TO6XUEv1+T68xXLe8/UOHEpgSglyRW9\nDYOElGRdYiMHinsomTaZthianGA+X6ZQNljXmwYEursydKcy7BnLEU8oKDGZdBNBknM2bIyMt+LA\nnJlDkaFsL58LerxYkwV2zTXX8MEPfpBrr70W8OLO3/CGN6x63fDwMH/3d3/Hpz71qcj3hw4dYnh4\nmHTac51efPHF7Ny5kze96U3H2/8WWnhZ4onpp9kzv4/B2DCHfpNl+4Z2Lq3K+L7YIEsiN7/1XL7w\nTzu5/Yd72TLUxiV9F/Gb6SfZM7+P30w9yeUDF5/ubrZwBuHIkSP86Ec/4otf/CLvfve7+dSnPsV/\n+k//6bdq8+jRo7z2ta+NfHfbbbcxPDzMVVddxQ033MD73/9+XNflT/7kT1YsJeIjn9cQDZmCqVEo\n6DhGCa3iGQ1FWyeuOmiaQd7RKNgVjIkKmrY2Sed8ocJsPlqzSMO7VhcsMlYZQRAQHYelkk7BqKBV\n5Z0lXEYnlkgnFBRJZE4tkJBrXohcUadcNqg4BoW8ZzgWCjqaUTtnRDP4qfAoudk0muG1658b9Ecz\nWHQKqGKMkm4gWSZl3SKH5+VKJFTy+QqCIATjzuU0ZpUC84USC/kSoiggCQKirjI249XILCT14PzF\nXJlklRAdGM+xLpZBET0jvmyXg74dm8wF1+QLFSzbDf5dKFQwQ6prhaLXft7VPG9JvIJmaxhlh1mx\nQK5kkC+Ug2e5ZBXZN36EYllD0wzGpxfIxFLkC1owD2GUbY3uHpVyxSRXrLVTMMuohnfN2LFFFq0y\nBcugkK8gy7U5OqIdJimlg+epmBViVcJc1DUMx6CiKWiageA4qBIoRQ3NMNFsg5xbRhFlxiqHMByD\nUklHMw1UEUqFCtm4xEy+TFnTmZsvRvrvP+P5Ja3mzRNlZmYLOI7LM4fmSCcU8mWdBXOaot24SZBz\nvbVZsotUpFLQ/vxiGbsMMUNjfDJHLlehZJbRdANJkLHd2voTJIVysYKYEcjlNERRCupV+XAsGaeg\nYTomml599mgsxAUKTfoFMLlQpqiZlMoGmxMFyorNdH6JQllDkyxiUpmSoWFYTsNzFQURx3WQjQoz\nswV0tURB05iPlVElk6SVpVDUqWgmFRzKhlF7hoLKYk5jhOmgXQmXhCwwt+T9LhzWD9OvbgjWy3LI\n5b2QSE0z0DSDwV44NLIQlFfw4bpu8DwnhHkWiqVAUn5OKbLn8CyGYyBIFfKWxohtY1QMJuYWg367\nmowiKsQtJ1jvuaUy+YJGvqARV2QqpvfcLNds2veyZWIJEnrMDMbeERukYFdos/tZyB9j/8gMkmpw\naMwj4AlZRJVFFsQSblkhX/DevVxeRKyuy7SapmgUEUWJ2Vlvg8Mfb7HozanGLLv2TTPcv3rkwVqw\npvC/P/3TP+WGG27gyJEjjI2NceONN/LJT35y1eve+MY3IkmN8pD18empVGpN8ekttNCCp0R174F/\nQREVZndvRZZEPvC7Z7+ovTkDXSnee/U2ShWLb/yrV8Pu+rPehSoq3H/woVZR4BYi6OrqQhAENm3a\nxL59++jr68Mwjq++TD0+85nPcPXVV0e+u+mmm7jqqqsAuO6667jvvvv453/+Z6655po1tekC+0Zz\njE4XcNya9LGPQDobN1AoWysKpagBGZbydlyHUf0AuqORK/mhYrWwr6JmMpercHSqwNSChmXbjE4X\nePbwPJpucXgiH2nbdt0IoQKCkg1hQ7ceiiyGvBRuoDAXhuO6kfyMIMQOLxner90URjgUzgkU6Oqu\nd92QRhkshIryTsyVIvFq9aGUQT0esS7/w9JZqCxWw9Rqx2bNSabKU4EIQcWy0K3lQ66otpuMyZEc\ntXAYpu24uK7DsZkiU4tljs1Gwys1pxaOGuRguS6GoxNTJJIxT0XQcd1AAMHPq3NxUBUpqNOlV+W3\nw+sxUCB0XGShcf89LATo+veuqi4WNZOinQsEOOoRHrMZCnHLFY1gXg3LrvbEOzcuRj1LIhKuC5s7\nvDy7XnkQyh2RjD+X5uF/whoivizLaQiBFACtKjwTXjOKoDIQG6ZH8eqvlZwCo4VjWI7N2EyR2UXP\nkE/IcTqlAYZim6i1WP0kCORLBhMLNVvYcVyWikakwPTa6t85VVnyGnSzUeQhHPJ3JDcSqdHlramQ\n/LsxzoGlI+wdXcRyTURBZCi2icpSG0OpdbTLNbXesAqkT6jAC3nMJBo3owQEBIGIuqQf1hcTYxiW\nw+hsjsn5GhGeXdKoNBlTOGrX70ezXL+THPVXu+daT9yyZQvXXnst11xzDW1tbezcufOEb3qi8ekt\ntNAC/PDoTyiaJbor55NfVHj76zYx1J1a/cIzHFdeNMgFW7rYc3SRX+6apCvRwZs3vZGiWeLBQz84\n3d1r4QzCtm3b+O///b9z+eWXc9ttt3HrrbdimisYsacJXmK3i1k10HxlKw9CqDipS7FqgCbEtb3L\nTl1IVXtarTvuMKGPhEhKcyuioBmUdJOJ+RKliskzh+bQ6wyyZkpZsuQr1DUvslnUTEzLwWZ50lXt\nWFMDx60zaMMhZLYT/tyY+zOljzFhHooWf60bg7vsP0JGdPX6+XwlOEW3jUDAoR6+EXcwd4hn559r\nOF67XViAIkQQQ5TAdT2vyXJIhsIVg9wx0xMaWNfjrSERT42wW+2pnufNW8FaQpHEoL6PXSX7kiig\nWRq6pVdFIry1K9WRqs5YF51irWaatylApBBxszpjfjv+Mce1IyUAfM+X7VqMzC5g2FagsKjUhQCK\ngogL9Ka7uaj3fI5NWViVOO1iLX+pPmepdmB5i1oSa/LmDg5jM0UWSp69atomjmMHCoE+LNckLiZI\niClk0fNWLRgLzBQXqZg2I9MFCmWD3UfmcY14IOMdFa3wzPGwII3tuEF//DlYrX4beIV7i1rt99B2\n7UjxZB/GCoWO5ysLGE7t2ZTsAiW7UB2vhSwoqGIM3XACEZHOTJwLNnc3bQ9AFCRes/Ec+tUN9Ku1\n3FW/0Hd4rP7c+PNSccoslb33oV9dj2OqnqJl0Yo+ixCrCtpooiJ8snOpfKwp/O8LX/gCDz/8cCSB\nVxAE7rjjjjXdpL7zW7ZsYWRkhHw+TzweZ+fOnfzhH/7hmtrq6Tl5lY9fLGiN+aWPtY53qjDDz8Yf\nIau0c3hnF1vXt3PD778CSXrxSY83G/Mn338x/+dXfsq9Dx/kqsuGec+r3swTs0/xyMRvuPbc/8C2\nrk1NWnrx4OW2rk8VPv/5z/PUU0+xdetWPvGJT/DrX/+a//k//+fp7lYD7LAimOuiO+EcDCfwDEzo\ntcTqHnWAY/rhwGsxFNvEuH6kse06wQFFbowKCcNdRvACQF+BkDouTFZFKPzwJqgl0VvLkCbfQ+aT\nruVInUtt1z/sbK9XCQuPNx+SU/eFKsLaa5pTYrA7RbuoMj1T/VogQp7WZFSJLjiwkNfJ4JBNqZi2\nzdj4UsN4wgaw47i4orusSptm6lDNhwl7L8t2kRljnIzUwVTZYHxpPshdCUMSBdozMUqBdLyHkp0n\nGZMDcpeIyxQ1k4rut+89xyVrns3SumpInR18L4oCBxYPR+6VKxtVNUCRHmWQC3s3cnA0T0IKG+Ru\nQERNxyRvzweCGZF+CyJ2lZC2p2Pkck7gvUzE5KoUvuut/4pDTIyjO1UvWp23wfNUeTcNK/TFxQR5\nf6kEj6j2rMYqB4nrA6tav7brMj5XZGxuiXlzJpD/BohJauTpB3W9BIGUkiKnF7DtsHEv8tzRxoLc\nYYIkNvFxWLYTGbWDw2BXirFx6MjEWCw0liwA710Pr8+R0gGGY1sbzluJVE2XmhcON1B2AAAgAElE\nQVTahcZC2VNV8p+KyySrtacGOlNMLpQaru1KdrC5R2ZyoURcTFJxysSlpDf+UAiu334qrkLIga8I\nKgkpRVxMYrgVJmcshjq8sbbLXbhuuLi4rwy4sn10MgnWmkjVI488wg9/+MOg6O/xwv9Reeihh9A0\njeuuu47/8l/+Cx/+8IdxXZfrrruO3t615YP4cZEvF/T0ZFpjfonjeMb7jWfvxXZsyiNbkUWZG3/3\nLBaa/HCd6VhpzDuu3MKd/3sff3PPk/zHd57Pjq3v4C+f/Cr/67Fv8qlLPvGirV31clzXpwqf+MQn\neNvb3oZhGFx99dUNYXtnAjrVbjalu5lbOAB4ynOaHQ5lc5sEpXjGo1sXWtQMYYNMQECVm78XZbuI\ngIDhLm9AVSyLcOCKZpcCr1qhbGBUd6JVIUbF1aphZd53Bauxtk20ny6DXSnGJ5Y5HvL6iAjolu55\noqrfqaKXdB72iPkkrFcdokOVcVgKPFG+HHgqJmMZy3vJpJD50650MlOaI5tSKWsmuaJHdERRwMGz\nYSzbC8+cm56nQ+lp8BjYjhuELzmOS0EzyeI21bS2HJNmpCoZkynpnkdgapn5akupdLXF6zxvzY1C\nVZFAM7FMP4QuGTxXWRJRhBgGNcNcEoTA0BZEbx3my1rQflJKIwkShaoXJC4mqDha1YD3iNWcOVnn\nka3BJ1rre1Polcaxa7pVJdlef31CBZ63oV3uYsnyFWGF5h7OyFsVlSwHz8syr8/RJjd/r8LhYxPz\nxcBTFkZKSS6rVS5LEoIAZd2i3amRrXqs60kjVRIcnPH+JjT7u2ZX15EPUXDp60owqKdIxDypccdx\nGa9uenQrA8yZk7i4bFnXxtyRqeqYbXS7MbTYr+nVm+phpjQbPWZHAyl9+HXCwiTXl8pPxELvVFqN\nkKqtQ23Bxs+GvjRzOY1edcjLQRMUhvsyDHQN0cUCqbhIX3sG1/W8xB2lnkCB0J9LQRCICYnq/b01\n3KH0kEn2o+HX8/Pn35tbTbcYnyuRSapUjNrvyaI1B5zVMD8ngjWRqvXr158wkxsaGuKee+4B4C1v\neUvw/ZVXXsmVV155Qm220MLLEYdzIzwzu5uY0c3SZBc7rtzMup706he+yPAfLhrkseemeGLfLE/s\nm+Xiszdxef/FPDb1BL8af5TXr3vt6o208JLGe97zHh566CG+9KUvccUVV/C2t72Nyy+//HR3K4Lu\nWB/j0xqyIKNTK0jrY/nciJqxkpTSy+ZKhvNvXFwkSWDzYBYBgcOTucDgnA4VDE5Kacp2bSdXEiRs\n16ag6UAtZ2XGHA8+F8pho64q1y0I2I7b4E1qBheXga4ULEeqqOX8+K6qJT0XhIL1qetAmEe3QmFi\nVS+ZIqjYWpyJwjhdbd6m75w5FcytGc5hq+tqOKRtfh5mDQ3TciiUTQQkwEYSCfxwLm61mLFOVu5o\nMLazSYVKlbzMVCXckzGzqZfRCoX8+cSiLaUSU6SmeWdh+MIdTiTUrvlz8E8xbBcF6FUHGa0cRBVj\niKLQ4P2RpHD4ldfuUc3bFPBDBedylYDUDsSGGa0cgOoz9PLYmoeDev2RSMVl5pxRssKmgDQN9aSC\n3LjlSLqAQLvcHZAq2zUbcuGaoWwXmhKjZeHW5sALyQ3lzuUqWLbD+d2dwGjTy3XD9shxxVqxEHRf\nR4Jivqak3Z6KI5o6hapQQ0yR0E07+N3winDb7Fs8EBTFjStRb6BaDSt0cEjFo+b9MW2Ui93ByO+J\nn3PYpmbBhZlyjViFbf7wPM+b00DzkLpMsjaeVEJBlSViqpff5xf79q6t1ZaSBIlXbu0hpnpjOXtd\nVAVclSXU0MZSeypGvXP88EQtd09wa3MSk2OgQ0rx7n10ypPur3/HyvbJ2+xcE6lqa2vj93//93nl\nK18ZUTz68pe/fNI60kILLSwP13X5l2peUf7gFi7c0s3vXb7hNPfq1EAUBD547XY+94+/4e6f7Oe8\nzZ28Y+ub2TX3HP9y+H/zyt4LyKgvPTLZwtrhb8pVKhV+9rOf8T/+x/9gcXGRhx9++HR3rQEdcg+6\no0UMaVg+5ESgVptHFaLSwKIg0iH3BIZNGFLIWBIRsOsM7YQqc1ZfJ0+P1UhVWmojZy0wly/TLie8\nXBsxFjG2NcOiS+lDFWOBwRtXJEq6FSr+GoUiqUCpOk4HRRYZ6koxnbdJJ5SAdHTH+hhbGgu8bD4J\nqNh6LaSq+r+It6G6iy7gkbuyblGe8cZlOHrQ+3DifT0B9HM1ZEEJDETdtFEVEcvwWujIyoxrJmW7\nSIwYkiBguy6jlYORttb3ppElEbHOiC6twZivOLXQu2R8ZZMsJWURqgqPohAOx3TRpcZCy34YoGna\nKJJHJBVBpa8zVj3mRqIiwzlR4c9hhA1YgGRMoaI7HJ7IIUtiA1ETBNg62MbhiTySIOP64gGCEBDK\npKrCKrlCIiKCIDAQ28CMMUFWXqYET4gUlcwS08Zsk3pgtbBBKyR60p3sQs8bFCreRkR4A2JmyRN9\nUUWVJ/fN4SyT+ui6BPWW6muohSEIQiSkdV13hjnNrJEqVYqISwiIdHc2KhwCDHanKJRMYpIMek34\nJgzLMdFtnbhcizir1UsTiEmebV8xbWRRqAtvbHw29c95oDMVCUGWJZFXndVYn8uHJIqYtsO67nRA\nqJpBUcTIBkgypjSQquUwlBogJsXojntrxXFd+tUNTBlRQlxf0Py3wZriaK644go+8YlP8LrXvY7L\nLrss+H8LLbTwwmDf4kEOLB3GXuqmUxrgI289N6Kw81LDQFeKN122gYW8zg8eHSGrZnjL5jehWRoP\nHPzX0929Fs4AHDx4kH/4h3/gr//6r2lvb/+tJdVPNrrbvd1RRVRZH2/MZ+jINNZSAc/ASUte/kas\nTvFsfWwLWbmj4Zpw0jfAYE+qIWywLa1GimtuiG8NRDFc18FxHebMKSZD+V1+OFtMTBAXk6zrbmPL\nYBtyYDRGjREXKGgmfbGBYDfYJ0PppMJQdwpFqfWhQ+miVx0KwgsRYKlkMJcrsVDxSYLQEBrlK8YJ\ngYEePe4bhGZV3azeAPROEhiOb2NdbHOgDmZXc1j8dse1saonBgzDXtYz57cvy/X9cImrEr3t0ec4\n0JkMPhuujoBAUkqhSGJEdKgzE+ecjTXy0KsOBgQhJseC575lKMuiOR+MfXP7Rm9eqsQo3G9FlEnE\nRXo7EggC9If6IodJ1Rr/vkiiiItHbPNlIyCr9RAlIbIm/ZBBRVAjRVmXg09842KSDfGtqGKsuacq\n9JW/Onvamtekao/X8qS2dWxhQ2YdcaE5W/JzBIUquatX1/MhCmKwftrlLjJy+7JjCoe0SqKIIkuB\nimdMkQLvIMBwX5ZkrDnpTsVkBrpSQV5WfU4VeAWtnxjfGxAp27FZqhY5FhGhumEwNlPk6HQhUrxa\ndyoM92fobU94Ne4SarDmOzMeSfM9xWvF2Rva6WlPMLiKyFZMliKkyhRWVkh1XJe+VB9DmUEkUaIv\n2YMkevPoui4xsbGf6eTJSylYk6fqne98J8eOHePgwYO87nWvY3JysmnV+RZaaOHkw3Vd7tvnEQln\n4mz+447zScXXVoD7xYzff80wv3p2kh88NsrrLhjgisFX8+uJnTw29QSvHbyMre0vbtGKFk4cb33r\nW5Ekibe//e3cfvvta87JfSGxdX07h8cak9N9rO9LMZqPeheGYhsB6Fb6aZM7g3yiNrkTx7WD8Lsw\netRBElIKqHlFkjEVUbAiBqYgRMmJLCh0dihMTfvC1eFdcc9g8kmTb6z1JXrRZYWFghce6IQ8VTEx\nTr5keF6oYi2Ey3ItTMeiYnvhe+GkcUFopDuzSxoFcYGBXinoS30IpB3yVHn/FQl7OtqrxT9976As\nKJiuQVeinQUtF6jaqdX5FNyqPLwDslTzYoUrwpRWCMurqsujSo2kKh1XyKTUwDt37uAgplDLNelt\nTzK/ZNKZTlBxi+HoTzKpxt/5RMzr1Pnd5+J0ujw+swSCE1AZ13WJS57hWJu32nPqziZwXYuYKnLO\ncCeLlcVAJEIM9V+sszPrC/iGEc6NWo6MyZJIkjSa470TruAZ/oLQ+HybISzk0JaKkSvpq+dU+bl6\ngoAieZ6R0NcICGxp34RmVTgyaqAoS5imQJ+6LhI2G4XX/rLlD9yQNL2WoVtpThj9eerOxr0NiKoi\n4XBfmpScpl3sY295hhljPHL+cpAEEbc6R07VU9WZiQf5TgCjc0ts61mgN9nNodwRtGqpElGoeoND\nSpr1EuyqJKKmVNpSKgsFHaPqzd00kGVdT4rkcdokiZjMlsG2Vc9TFDGS/yjXL8w6OA6szww2P+g2\nbrD0dSQjYYu/LdZEz77//e/zsY99jC9+8Yvkcjmuv/56HnzwwZPWiRZaaGF5PHZsN5PaBPZCHx9+\nw+UNBfxeqoirMtdduQXTcvjOTw8iiRLvPfudAHxn/3cjssotvLzwF3/xF3z3u9/lQx/60BlJqMDb\nad68gtEwmveMtvCfeFWsGcM+oQLoVHrpVgeCf4e9GaqgMtCZCvKROhOdXNRzXoOamBcu5nkLEmKS\nnvYEw31eKRMvryn8PlWNwkBavOqJESU2tW0IwmnCUusubrW2UNQALNl5npl5NmhMaRDUCBvB3n8q\nTrkWkiZEQ8ryZZOlamK6T37CY82mVLpDu+aSIAfeHUEQaEt68xqWrvbH5zhelolfE2klYz8Vk4mL\nCdbHt0TOC3t+YopIJqVEnkRKSRJGNqlwzoZO4lUvRHjufDXBoXQv/aoX7p0ISamLooAoCFSsSjCN\nrlvz3IUj+LrbElx8Vg99nV7otB3yZgx2p9gymI30U1zFePWhV0Ms/VC5ek9VVupElVQkUUASZOJ4\n91dl0VNHbCrX0gh/Q+HCLd1sHqyW4GnmOGz2neDlbaUTSt3XAh3xdgZSfVRMi0LZD61cPhzN72+v\nOkRbkxBEb/NiGe8ocO7GTjb1ZxFFgc1tG+nIxBjqTmHYRlVVT0CVZeJyLLKupVVIVdhD5lSfbf14\n/fMA8notj0jwSVXovJVq5smSiCCIxFUZRRaPm1AdDzxCLCGJIht6M8uGpfowTJtcyaBiWI3155qc\nn0kqa1yBa+zvWk762te+xt13300qlaKrq4sHHniAW2+99SR2o4UWWmiGcsXk7t0PAXDl4FW8+tz+\n09yjFxavfkU/WwazPL5vln2ji2xuG+Y1A5cyXpzk58ceOd3da+E04eyzzz7dXVgTetsTtCW9kKde\n1avr0yFH67hs6MtESMBaIMsiWwaz9Hcm+Z1zNjDcn2Fd2tud7Yi1RQwsHy6e8bQuvpn+2AZE0TNY\nJFHAdZ2IJyJsEHYpfciCZzT5Bv+mtmHWx7fUwvbw6mv5HEsV4g2hiz7qbaLwvSJ5HK5HJsP3BZhe\nLAfhbLU6NKFaPlK01Gc4L00QIKN4BrkS+t7fCU9JWXqFzaSkbND+5sEsbaloKGV/Z5L2TIxOpRdZ\nUIJEeIBMyJBNt1kNO+uK1BggZEXmvgZ/3K8/+1x60m30dSQZSPfTk+wOnR+dA9d1A29g2AD11df8\nY+FNKUkQGjwhazEOFUkKwsTCxYjDSIhJXtG1nbPbtns5YNUl05mNIUsCg121/FifBCiSGBSXBtjU\nnwkIhiyJQV/DRrM/D+NzpWB9hL0tiiTSllJJiKmah6s6ZKsuN1BqIgdfu4+HlJQhIabpzMbZ0JuJ\nnLGSVymbVOmrEu+UkmRbxxYAepO9QdmDmKzSkYlFxCBWInoAMSmGJMhVQQsDy7Eacsmgubz4s4cW\nGJspLiudU49MUmGgM805GxpDkU8FLtney+aBDDFFjKxpqQnxtxyH50cWePrgHAePRfP/mpU4EIDt\nXSdH+Q/WSKpEUSSdri383t7eNe1iuK7L5z73Oa6//npuvPFGxsbGIsf/5V/+hXe9611cd9113H33\n3cfZ9RZaeGnDtGz+/F9/hBVbpNPZyHtf88rT3aUXHKIg8L5rvB+87zx8CNd1eceWN5OSkzx05Ecs\n6blVWmihhdVx6623csMNN3DjjTfyjne8g9e97nUN53zxi1/k3e9+NzfeeCM33nhjpID9SjhrQzsX\nbunm6vO3sSmxvaGQqiqLdKRjTWP9V4IoCLxm/YVBjbr+VC/ndZ9LR9zL4agPjXIdN2JQyaLoCV9k\nYtXwP8+gy6bUiGBCOIfLN2hURUQWFFzb+/e62GavMGnIs9Wn1orD1iMVlxmI+UI7NSMnKuMMstBI\nQDqUnuo9CDxt4ZpI9XZkUkpHjvXG++lW+mmXu2rfCwLbUufQow4iCiIxMU6/ugFB8AiHV/upNneZ\nhMIrey4MiGP97rlvt4l1drAoiCj1XwK2YwWGeNjoC7d63qYuNg1kGUoPMJyN1gz1zq15F/2+xhSJ\nzkyc7SHj18/TcVwnEDPojEc9LjE5mu/XzHi9+KweXrmtm4TiE05f0S36zFxAEiV627zn4IdmKgps\n7M/QkY4Hfe/rSDLUk2Jjf4a2cDFroUYqJFEI5jcsxuAXoxYQyJcMKqaNrsmhnnkfwh5g/771Kn0r\nle3QQnLxoiAgSyIxRWS4LxP5vpmX8+z1jSSkLZblkv5XRvLKOmMdiKJAR6rW15U8ND3Jbja1bcCt\n5qjpjs5seQ5ZFpsSqzAs28GwHBbylcADvBoEvGe1ksDEycaFvecD0BfaUKgveF6PcOgjNC8yDs1r\nhJ0o1tTStm3buOuuu7Asi+eff57PfOYzbN++fdXrfvzjH2MYBvfccw+33HJLg1rgV77yFW6//Xa+\n9a1v8U//9E8UCi+fGi4ttLASbMfhqw/uZkp5GoCbL3vHmuLOX4rYPJjlkrN7ODKZ58n9s6TVFG/f\nei26bXDfge+d7u618BLAzTffzJ133skdd9xBf38/f/7nf95wznPPPcc3vvEN7rjjDu64447IRuNK\nkEQxUr8lbOT7WJcZpK8qOjDUnV42IT1A1TjwFbt8xEPGcDhRXZZEkgklYqXHVAlBEBGrQgO2a9Hb\nnqCnLcGG3kwkzMyHb9jFq8ZUrzzMQGwDiqiiiGrEyF1OsACgMxsnLvphcM0tHcd1UQWPaIbl1H15\nZVkUgyT3sGegXixCRIzk0KiyTEZub/g9vXBrjxdGWUVCSgbzKYoCQ7FoDqe8QsH1jf0Zhvsy0XA6\nQaRbGUAWG0OlfL/bb4MwDxAEgbSaRgC6srGI8esn7duuHayRdZnBiCqcUtfHTFKhtyMatqjIEqIo\ncOUWb7Mvby0ya0wEdYyCvlTH1Z6OccHmbga7q+GHTk15zocoQLK67qQQiRAEITB8BYEQqQr3xw95\nFIlVBlBL62pCDwHx9MhZruiHj3rf1+cP1Rcu7s80VxpUhThdsU62dWwJ3g0vlLYxrHGoO72sOE1D\nu9X32rRqA1ypgO1wdj2qpJKMyZ7Coz8XeMqLablG2OpD4nTLCfIWfTGOteCFrhepiDIX913EcHY9\nA50p1vdmliVJy2G580+mbbWmWfnsZz/L9PQ0sViMT3/606TTaT73uc+tet0TTzzBFVdcAcCFF17I\n7t27I8e3b99OLpdD16sL/GVqNLbQQhiO63L7D/axa2YvYjrHhd3nsT67TOLlywTvfP1mREHg/l8c\nxnYcXjNwKZuywzw1s4vn5ved7u618P+zd+fxUVV348c/986+ZbJNFkI2QsJOhIgbIiCguKAgQQUL\ndflZ4SnWB7WlWqVqoWi1j12sVetGqRRkU+rSKogbbhALyK7shC37NpNMZub+/pjMZCaZJBOSkO28\nXy9fkrl37px77izne88533Oe5efnc8cdd3DVVVdx9uxZ5syZw4kTTU0sD98HH3yA1Wrl0ksvDXpc\nURSOHj3KokWLmDlzJmvXrj3n11BJajJs8f6/h9mGkGCK9zcAE6KNDEiJom9s00Gbr+Og+d/M+hZE\nYowRucH+Oq0KGQmV5O21qFWcWE1a73wQSQ7ZxvclyNGoVeg1alwuyR8c+QIgX0+Ar1EZE9G4B06n\nUWGry4jXMJ1xhDoKi8qKQTahqVt3J7DnxJ8KPSB48j1mrFsQNZAUlBij6bkuklTf2wGQ1TfSv69J\nY/QPgQQYbhsa1OhvWFlqWW60GLNNk4hJ1cSckIBGuFYtE2nW+efNxZtjG+8fwJdi23dUX4Neq6ov\nryEgYPI1hr3DPb3vEZUkk2Cqn5voHTom+YN7vVYV1Fg06gKOrdX6100KtR5U4HvOqFf73+e+4aay\nJIfM/hc4hyhaFxW06Kt/qGPwK/n/pZV1eDyNA3vf3CG3oviHw7rcHvYfL2m0X6AofUSj8vn262tO\nwqqL8A/7tBq16LWqoPdZcpyFJFvzWe4CaWRvfSbUzX+TkBoFVdGGxoFevz5WEqNNRJmDgzejykSM\nxvud0zAroNut1NVT6M9FqGyjAHIT+3ck33VJTbCQFGtq1fq5RWXVVNeGTjbTngFiWEcyGo088MAD\nrF27lvXr17Nw4cKw7tJVVlZisdR/WNRqNZ6AFKyZmZlMnz6dKVOmMG7cuLDv/AlCT7Zm80E+/+4k\n5jTvopHXpk/s5BJ1vsQYE5cPT+RUkZ0t351GlmRmDrwJWZJZtX89Tnf4d9iE7m/RokXcddddmEwm\nbDYb119/PQsXLgzruWvWrGHKlClB//lu+L300kvMnz+/0XPsdjuzZ8/m6aef5uWXX2bFihUcOHDg\nnMsfZa6fg9OwsSTL3qCjb5yZ4f0aN6gHpkS1mAELghucoZo/Oo0KWZKRVd41oAIzuDlq7UEJJRKi\njSTHWYIe0zcY+iNLMumGLBLrEiqY9FquHTqS7KSMRq8tgT/zV8MhfhGqKG9Sjsr64XlpEd5jevzP\nxp+mvF8fq7/xrNWogs5VITiIkiQpKKgJDMAaBjvREXp/QObr3UnQppCkS0Or0gQtlOvrMVTJTfcw\nSnW9H3pVfYM3zli/jk9gO95m1fsDmvSo8NYj9A0D9SYRkAi86oHr+vnq2+VxB/XgBepjTmBwzEBG\npWaREG3EYtD4U+HrtWoGpQY3tKVmhqb1iQ5u1/l6FV3+YFAi1hDDwOjgeS1yXf2m6PuTUZcivuEx\nAhvVoRrYDec2SQHnWljmzcZY1ERChsAkFPGmpgNb3/GMeg0jMm30sRnQaVRk9KlPpR4fZQgrRX1G\nZDopEX39wUPfGCvx2r701fdDavCZ14bo8dSoZaIj9I3mLXrDS+/zj5QdpcBe5N/iSxbSVOliNPHo\nGiwyrFPr/MOMO1PgMmBZfUOXx9cz90N+01MFwk2WEo6wUqoPHDiwUeRus9n49NNPm32e2Wymqqo+\ndajH4/HPxdq/fz8ff/wxH330EUajkQcffJD//Oc/XH311c0e02brHZnPAolz7vl857tu8/f8+5tj\nxCXbqdAVMyopmxH9usek/NZq7TW+88ahfLX7NP/64ghTxvbHZsvi+vIJbNj3IZ+c/YxZw6d2UEnb\nT297X3eUkpISLr/8cp555hkkSeLmm2/mjTfeCOu5ubm55ObmNnr84MGDWK3WkMuFGAwGZs+ejU6n\nQ6fTcckll7Bv3z6yspqf4Nzwek+4WI+jxkWZ+yyeuvH+8XHexBIRlrK6vyOCfm+PFHh/QwemRRNr\n1aNSyejL+uNRPNgim34/XTY0ja++PwhARIQBrUZFlNVIWd1N+aREK5IkYauyYLc7cCm1WCLqG8C1\nQK0HIiwGLhiUELSwJ8Dpsho8DRt6GhlZ7Q0wYqx6+vf1NkZLjxbRkM1mgeNl2KzRGF1a8h1HAbCY\nDGjreqh8oWeCLZKTLgMej4Ls0OPBREy0CZvNgs1mYX/xcdzVDkxGLZaI+oC1xgM6tYHqGi1OD9hi\nzcRFmamo8TbojXo1strbDIqzWdAbdZRVu+lj8x470mVA7VQwqcyU1xqIqCuR77pG5HunLOT0S+Nk\n+RkMGh3fFx0JeT3kaiN6lYF4WySpSZcjSRL5FadxlHjn5mXGJ1Nzxk6sMYpCe33PiVpWNfu9YbF7\ny5TVJ5Xtxw4TFaHHZrNQKpuo1TjQqbVBz1fZ3RRTgMWqpVarhxoXcXERuCuqsSjeYw1I9gZyKdio\nUHlToFuAkYPTMOjUjdqDaTHJHCyq7ym2mrRUVDnpE2dmWHocEfr616/RVlIplxEZpceiGIg2mbDF\nWojxmMh31c+71zjdlFTVEmk2ExdnISK/PKjurRFlRNQlELHZLEQUVKGpDu6JqKkxUOu0Y43QY9Rr\n0DrdyLUG7NVaatwQHWXCWaEjwhKcVOWiIQl8vUtPWa0Fk9pCTLQZw5n6obbjR6WSt+8sADExZmxR\n9c+vLCjEZa/BqNFTUet9PCE+IqyRWDYaX+cLMlIpKqvGEFECTu/x+ljiMWr0VKmC6wSgCCNue/Dc\nqBK7jMVspLLa+/kopsD/OXEqYNUaUbvclDsaz1EaPyqV7QUl+IL0PpZ4+kWHF+h3tJOl1SiyjNWs\nZUCGjTibhZ0/FAbtozfqsJp1xERX4qwb5mmg/jwtEQbibaF7Is9FWEHVvn37/P+ura1l48aNbN++\nvcXnjRw5ks2bNzN58mS2b98e9ANksVgwGAxotVokSSI6Opry8pZXHy8o6F3zrmw2izjnHs53vp/v\nPMVr7+0l0qIlKnMPFZVwZZ+xPbIuzvUaX5nTl39/fYw1G/cz6cJkxsWP5fMj29iw70OGWIbQx9x1\nsyP2xvd1R9Hr9Zw+fdrfUNm2bRtabfOTllvyxRdf+IerN3T48GEWLFjA22+/jcvlIi8vj5tuuqnF\nYzZ1vcsra6iorLtTXugNmsorvH8XFgYnwPA9bq+sptjtbRTo6xpfzb2f4tQ2DK4iimvPUllZjUYl\nU+KxU14hBb2Ow+4kyiJR65GpKHf4n19T7YIaNeU4KC2xNzp+aamd8qqmJ7Yb1JK/fCqnDpfHhUlj\n5EzV2aCye89PhcPh7W2uVKpRS8FzXCpLazG6LRhkI6cqi7CrnFSUO/zHcLLDfgUAACAASURBVNid\nOGqcVMpgDFhcWFI8VFY5sZhVuDxqdBJUlDv8daqR9JTXBbe++ugXb0KrUVFQUEFpmR1HrQNFraa8\nor655Htdd60Lg1ZNWXE1JqyUVJYG1WGgeEs8VZUeKssdVNW9b0ur7FTUlaXWKNFfn4nDWU1F+cmg\n5zZ3nX2v11ejx6iWqbE7KSiooKzcQYXdgUPlokBb//yq2hoqyh18V/49erWeancNBQUVFFaV+8sS\n+HqB51NlrKYqRFF0zgj/9QOwWbT0iTaAy01hUSU1AR9N3zmfVbyvp3M5KFAq8CieoNfyAA6Hkwqq\nKSys9F8zX9kqKqpx1w3nKiiooLTU3mhulL3WiU4PbqeLCqcLp8tDdZUWh9Nb1mP5ZSgOd6P3cXmp\nnYrKamQM6NQqzhZUBp1feVn9e6ikpApc9cFccUklFTUOXBrJX58NP9OtEW3UEG3UsP3sEVwe7+uY\nDFaKK0v89RV4vUpK7VRUN3wPGqmqqsbh9H5uyqqcqNUyJp2a8opqZJcDh7vGXy/gXSsvu38sjqoa\nKsrre/NK3FUUuLvG71htdS3lFQ5MGtlfBxF6FScK6uv78LFikmxmSku932Euj4eaahcyKlxKLRXl\nDgoLK0mIb5+et7CCqkAajYZrrrmGF154ocV9J02axJYtW7j11lsBWLp0Ke+88w4Oh4MZM2Zw8803\nM2vWLLRaLSkpKUybNq31ZyAIPcCuw0W8/v4+THo1N11r5Z+HjzE0ZhAplr6dXbQu5ZqLU9j8bT7v\nfXmUsdl90Gm03JI1lb/ufI0V+9Zyf8688z6BVjj/fvnLX3LPPfdw7NgxbrzxRsrKyvjDH/7QpmMe\nOXKEyy67LOix119/ndTUVMaPH8/UqVOZMWMGGo2GadOmkZHReFhbuEJNOs/JsjVK7RyotVOOZUnG\nqo6muPZs0FDAgSlRQUPdNLIaj6oGlSq4J0qjkrAFrI3VUOAxDVo1DmdwL0HgUEHfQt35ladCHivK\nrONw6FjE/1rJliRqat1o5UrskhQ0dM43tKlh7WlVMhcMiKfAoabAXohFa0J21597X5uZovLg4V/6\ngAQdviFlarWKWKvBP2TMZ1i/mKC/G85XCWSzGkmOCZ7r0rD3QtUgM2Bzwwl9MiLTcSvuRt979dkA\ng8ukCThmtavaP8TRrQTPbWuNwKFtKXHe1Nc+DRMj+M7RFyDUL+AcMCdKpcXpdhJl0dHHGnoukkYl\nU+OsL3Oo6TWyJGMy1kd0Nn0chQHnf+R0OUlmI7IkoSgB1y9wKGakIejzEqtJbDCfLpjvfGVJwqBV\nN8oseK48Ae8tWZLrk3A0Qa/We9cvw3d96svsW4g6M8nqT4nfUIQ6ijhj42GP7TlUrq2S48yYDGqi\nLYFzBoPLV+nwDmv2KAoatYzLAym6TADK3SVYdfpGn7u2CCuoeuutt/z/VhSF77//Ho2m5cW+JEni\n8ccfD3osPb0+g86tt97qD7gEobc6cqqc59fvQpYl7p0+jA1nVgBwXfqkTi5Z12Mxapl4YV/e/fIo\nH28/yVWjkhkaO4icuGzyzu7g8/yvuaLvpS0fSOjWhg8fzpo1azhy5Ahut5t+/fq1uafq0UcfbfTY\n7bff7v/3nXfeyZ133tmm1/AJteaMRq1C08wv8rk0ZQYkRxFTPpwylXdonaIoRDbIQKaW1dTQuMdJ\nrZZxh/GqOrWKvjYz3+eXBj+/mex4DSPEuCgjFNRtCvWa3slReDwKaknDoIghpETUL6zc3I0USZJJ\ntiQRqbMSobX4G1lqWQ6xEHETx0AiI8lKYZmj2flsQQGMJAW19EOVMUYfTXF1KYmmhID9gueAtcQ3\nt6XhvFJ1XfAQmLACGmf284nUWTlVeZokS+uTIqXEm9lVqibaogsKqADMDRY79gUDvh7LwAQUPrGG\nGE5WniI2Qk9KfOgeb7NRQ1F5NY4ab3AWak6VhBxUn2mRSRSdPRu0vbK6Fo1KRW1dL3CfGFPQc2RZ\nIsqiw2rSUlblxKK2BgVZDeOrZEsffig9TIqlL8ao0Gu1nQt/QpK6sll1EUQborAZQs/38iX0UBSF\nYalJfLX7CJIEDUO8hgt8A5hVEc0Ejl0nqJJliVhrcB03/MiUVNaw82AhHkVBrZKpqXXX16E6msyo\n9h3dElZQ9fXXXwf9HRUVxbPPPtuuBRGE3qikooalb3xLtdPN3BuH4DKe5XD5UYbHDiElQvRShXL1\nRSlsyjvBe18dZewFfdBpVEzPvIE9xQd4++D7DLcNJlJnbflAQrfz0EMPNbu94bIdXVVLd5kD+XqB\nVM0FKU2IsuiIsujIO3OsyUxZgYkiYo0xFNZNYldJEomxFsyGJoLVuuNpNaqQGe0CM+nVPyV08nBJ\n8t51Pn429DApj6IgI3G2pK6nqMEB1L76bKK3wpthzjtvwmLU0r+PFYtR23zgR/2ivL6G2oUD4ppN\nOBDYK3Nh/AV8V7jHnw4+VO+kSlYxMDoz6DFjQBDSmuar1OD4CaY43IqnUW9Do0Wh63qoTBojI+Oz\nz6mnX6tR0Te2cY+STq1r1AugbtD7Fir1fqieg/59rGgCEib4Uv5/s/u0fyheQ96gqv5vjVr2Pq9u\nd9/nUKWS8I0cbJiUwVddDR9vtEMdo8bIcNuQ0Pu2QaI5gVOVpxkU7Z1jLUsy/axpjfbzvc9Ukooh\nMQPxKB5S4q18d1CDVCs1+h6odXnw5lCpPw+btk+TPeNdJ6QKLfDzaTFoqXA4sdcF3upmehjbS1hB\nVXf5oRKE7sRZ6+a5dTspLHUwfWw/Rg2M4+m8NwG4VvRSNcls0DDxwr6888VRNn+bz+SLU7DqLEzL\nuJYV+9ey+sDb3D1sTmcXU+gAF110UWcXoV2EWky1KcP6xVDtdDXdqGsjjaq+GRCps+JRFIod3uQE\nfePMLTeyJULe1W4pYAk6hCSh16i86/jUhgrGvP8/Veydf1ZaEdwr0yc2gvzj+ViMjXthQqV+jo0M\nrwfBqDZQ7q4lSuftDWrpnJQG/QADozLZUeDNLNmaJWNiDNEUOYpbNdSq4XnKkkxyE71Ovh6MRsdo\n56HTocrf8IZCqCA11GKsDa+ZRtN4H1mSggJbWZIbZSYMvIa+9ajUsoTFqKXC7sSgbxD01ZVvYFwq\nmtpKMhOD596cryCjjymBRFN8i9coydwHj6KQZE4MWrdOJcl4FIWjp+rnQ7kVxTts1xDqRkdTSw90\n7bAq8F2t16kIjLfP5cZUa4UVVF155ZUhK1JRFCRJYtOmTe1eMEHoyRRF4fV/7+PwqQquvDCZay9J\nZUfhbo6WH+cC27AmfwwFr6tGeXur3v/6KONHJKHTqri0zyi+Pv0t2wt2saNgF9m2oZ1dTKGdBc67\n3bt3L1999RUqlYrRo0e3aY5TVybLEkZ9y8PtwxGqryqwp8qkNlIm1SeMaq5RnxhrouyY0ztcKkRQ\nFSoI9DUIGw5B8z1bq1Z50w62UPKGL9cnKoIMtzVEKumWG4E5WbYmt6VFpGB32cPu+TZrvL01sUbv\nXCtNwNC71gQsvrk9rWnAtub4Q2IGsqtwb9j7tyd1g16oUMMRJQmSI/qGTBvuo1U3fn+pVTJOV32y\nCt+itgAWrXcYYVqihZ3FvtfxpcyXyEyOxF7jIsIY3DPruwRJlgSSBjYux/mKMXxrbLVEq9I0SkEP\n9UsGuJtMQ98wKK//O0IXQXlNy4nkugJ3wBwxfYMFzFtzo+dchfUKU6ZMYdq0afzzn/9k9erVzJkz\nhxEjRvhXoBcEoXX+/c0xvtp9how+Efw0NxuP4mHDwfeRJZkb+jW/rIBQ11uVk0yFvZbN/80HvI2K\nWQNvQi2pWLX/LRyuZma+C93aq6++yn333cfZs2c5ceIE8+bNa9OCvD1dRmQ6apUm5MTzwMa4RqXB\nFDj8rJkWY6RZx8WD4omy6BoFMylxFgy6xvds4402bMZYMqOCA2Dfy1iMGvr3sTZqDOUdKKC8yoml\nrsGblRLcW2DUGNCpQw9TbGmopXcuW+h9tCpNq4YSe4d+DSXV0jgtf2s0tX5Uc1oTgDU1r+p8UMvq\noAQcDYMs8J53vNHW7FpIoebDmRr0MullIxFaM6kRKWTVvedMeg0G2Rv8aqS69cVUMmqV3CigAkLf\niQjQVKKHrkYjNb7mgTFVw17W2Mj65A++RDPQtRJVhKIEXA9dg/eITqvigv6xJMd1XGbasIKqzz77\njPnz5xMXF0d0dDQ//vGPOXToEElJSSQlJTX5PEVR+PWvf82tt97KnDlzOH78eND2nTt3ctttt3Hb\nbbdx33334XSKBTyFnm/nwSLWbD5IlEXHT28ahlaj4qvT2zhjL+DSxFHEB6xsLzRt0qhkDDoV//76\nqD+VboIpnqtSx1PmLOdfh/7TySUUOsqqVatYt24dCxcu5OGHH2b16tW88sornV2ssEVoLRg0RtKs\nqefl9SJ1Vi6wDfUvUhuo4fwV3x39cDTVkDcZQjfaVbKK1IhkDGp90OP+ZAWALdKIJsQd5T1Hi+uP\nH6LnLts2hGEh5rKc7+FKWpWmza95Lj1VrdGe2c58IsNcDFaWZEbEDfOfW+gAL4wemRBBlUGnDkok\nIksyA6MzsRljgurysrTBpOgzg+ZUNRRl9g6d04YYZhioe4RUwcN8fTwBcZRvbltshIHh/WJJCpgj\nJ0syA6IzidBZQt6Y6Up8PVVyg4W+ARJjjOi1ajQhrnd7Cbsv7IsvvvD/e/PmzZhModNcBtq4cSNO\np5OVK1fywAMPNJqbtWjRIp588kneeOMNxowZw8mTJ5s4kiD0DGeK7by4YTcqlcz8m4YRadZRXVvN\nu4c+RCNruDZ9YmcXsdswGzRMyEmm3F7Lx3W9VQBXpV1JvNHGpye+5Ej5sU4sodBRrFYranV9I8Fo\nNIb1m9RVyJLMkJgBxBqiO7sojeavqFuRRMPHoAtOa3wuTZbhtiEMiM70zoNp4gCKR2k2UUSooLGz\npVtTSYloXc9V/bCs1tWkzRhLgik+rH37Wpq+IR4oPUQyhFAyrGlcEDfM/7dBYyS9mZsGg2MGkhzR\nF4vW3GhbOLFkqJ4qjVpuMQgCiIsyMSi5Ph1+qKGqmcmRjOhva9Rr6pOdEUuyzUykWRdye1cTaoiu\nL42+QatGJ+uJ1SSSZc3CqG+8wLNFayYrqn+jRCNdja1u7l2/PsELLgdldezAGy1h1c4TTzzBwoUL\nKSz0rlTcr18/nnrqqRafl5eX519MMTs7m127dvm3HT58mMjISF577TW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2xnH//ve/KS0t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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1296b0cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.traceplot(trace[1000:], varnames=['p_retro_1000', 'p_prosp_1000', 'p_rct_1000', 'α', 'μ', 'σ']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Summary statistics from each of the above parameters."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"p_retro_1000:\n",
"\n",
" Mean SD MC Error 95% HPD interval\n",
" -------------------------------------------------------------------\n",
" \n",
" 0.7946 0.1931 0.0113 [0.4376, 1.1639]\n",
"\n",
" Posterior quantiles:\n",
" 2.5 25 50 75 97.5\n",
" |--------------|==============|==============|--------------|\n",
" \n",
" 0.4451 0.6585 0.7891 0.9241 1.1779\n",
"\n",
"\n",
"p_prosp_1000:\n",
"\n",
" Mean SD MC Error 95% HPD interval\n",
" -------------------------------------------------------------------\n",
" \n",
" 0.5657 0.4015 0.0174 [0.0169, 1.3539]\n",
"\n",
" Posterior quantiles:\n",
" 2.5 25 50 75 97.5\n",
" |--------------|==============|==============|--------------|\n",
" \n",
" 0.0746 0.2845 0.4725 0.7486 1.5540\n",
"\n",
"\n",
"p_rct_1000:\n",
"\n",
" Mean SD MC Error 95% HPD interval\n",
" -------------------------------------------------------------------\n",
" \n",
" 0.0352 0.1493 0.0087 [0.0000, 0.1788]\n",
"\n",
" Posterior quantiles:\n",
" 2.5 25 50 75 97.5\n",
" |--------------|==============|==============|--------------|\n",
" \n",
" 0.0000 0.0000 0.0001 0.0061 0.3406\n",
"\n",
"\n",
"μ:\n",
"\n",
" Mean SD MC Error 95% HPD interval\n",
" -------------------------------------------------------------------\n",
" \n",
" -7.1682 0.2568 0.0156 [-7.6702, -6.7266]\n",
"\n",
" Posterior quantiles:\n",
" 2.5 25 50 75 97.5\n",
" |--------------|==============|==============|--------------|\n",
" \n",
" -7.7168 -7.3248 -7.1438 -6.9858 -6.7428\n",
"\n",
"\n",
"σ:\n",
"\n",
" Mean SD MC Error 95% HPD interval\n",
" -------------------------------------------------------------------\n",
" \n",
" 0.8604 0.2499 0.0216 [0.3881, 1.3700]\n",
"\n",
" Posterior quantiles:\n",
" 2.5 25 50 75 97.5\n",
" |--------------|==============|==============|--------------|\n",
" \n",
" 0.4307 0.6958 0.8348 1.0013 1.4240\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/numpy/core/fromnumeric.py:225: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" return reshape(newshape, order=order)\n"
]
}
],
"source": [
"pm.summary(trace[1000:], varnames=['p_retro_1000', 'p_prosp_1000', 'p_rct_1000', 'μ', 'σ'], roundto=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By comparison, estimates from Pritts' supplement:\n",
"\n",
"![pritts estimates](pritts_table.png)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p_vars = ['p_retro_1000', 'p_prosp_1000', 'p_rct_1000']\n",
"intervals = [pm.stats.hpd(trace[var]) for var in p_vars]"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kt956S8eOHVOzZs0cc+T3v/+9pCunV05KStL+/ft1/Phx9e/fX8YY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\nBwDAgihwAAAsiAIHAMCCKHAAACzo/wPf3YJp8DpxVAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12fdb6908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"meds = [np.median(trace[var]) for var in p_vars]\n",
"upper = [i[1] - m for m,i in zip(meds, intervals)]\n",
"lower = [m - i[0] for m,i in zip(meds, intervals)]\n",
"\n",
"plt.errorbar(np.arange(3)-0.1, meds, yerr=[lower, upper], fmt='o', label='Updated MA')\n",
"plt.errorbar(np.arange(2)+0.1, [0.57, 0.12], yerr=[[0.57-0.17, 0.12], [1.13 - 0.57,0.75 - 0.12]], color='r', fmt='o', label='Pritts MA')\n",
"plt.xticks(range(3), ['Retrospective', 'Prospective', 'RCT'])\n",
"plt.ylabel('Rate per 1000 surgeries')\n",
"plt.xlim(-0.5, 2.25)\n",
"plt.legend();"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.gridspec.GridSpec at 0x124f61630>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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RRBNj+43Hh/SCDzXU1hrF4zGtX78j55VcTJKR5E4ySfLjekj+1IHKIp0UjiCa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\neykWk8aNa+QVHeAI0klhoIm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"text/plain": [
"<matplotlib.figure.Figure at 0x127367b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.forestplot(trace[1000:], varnames=['age_max_missing'], ylabels=[''])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Goodness of fit\n",
"\n",
"To check how well the model fits the data, I conducted posteior predictive checks. This simulates data from the model for each data point (*i.e.* study) in the meta-analysis, and compares the distribution of these simulated values (here, 500 replicates) with the value of the data itself. If the percentile of the datum is very extreme (either large or small), then it is evidence that the model does not adequately fit the data. The distribution of percentiles in the bottom histogram shows that there is no evidence of lack of fit."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ppc = pm.sample_ppc(trace, model=pritts_update, samples=500)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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777+vJ554ItibSdvuuBUrVmjGjBknLY/2/pKSkrR3717l5+dr/vz5mjJlijGv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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120a850f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import percentileofscore\n",
" \n",
"p = [percentileofscore(s, o).round(2) for s,o in zip(ppc['obs'].T, tumors)]\n",
" \n",
"plt.hist(p)\n",
"plt.xlabel('percentile')\n",
"plt.ylabel('frequency');"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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