{"id":"W2031813555","doi":"10.1161/circulationaha.113.001953","title":"Identifying Locations for Public Access Defibrillators Using Mathematical Optimization","year":2013,"lang":"en","type":"article","venue":"Circulation","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Heart, Lung, and Blood Institute; Heart and Stroke Foundation of Canada; Institute of Circulatory and Respiratory Health; University of Toronto; American Heart Association","keywords":"Automated external defibrillator; Medicine; Software deployment; Population; Medical emergency; Emergency medical services; Geospatial analysis; Emergency medicine; Cardiopulmonary resuscitation; Resuscitation; Computer science; Cartography; Environmental health; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001195568,0.00007604519,0.000133296,0.0001592744,0.0001622769,0.0001751412,0.00003294541,0.00007757854,0.00009960013],"category_scores_gemma":[0.0002108916,0.00007260071,0.0000870086,0.0002504271,0.00002757482,0.0006685917,0.00001604855,0.00003734419,0.00003432501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001102237,"about_ca_system_score_gemma":0.00006722634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001126479,"about_ca_topic_score_gemma":6.752821e-7,"domain_scores_codex":[0.9992883,0.00001833649,0.0002279078,0.0001558085,0.0001687797,0.0001408434],"domain_scores_gemma":[0.9992948,0.00006256038,0.0000900076,0.0001494769,0.0003242457,0.00007888867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002994719,0.0001655791,0.6398553,0.001080489,0.00023139,9.122058e-7,0.0008972274,0.2955027,0.02378228,0.02243268,0.0008736824,0.01514792],"study_design_scores_gemma":[0.0005071557,0.000003976365,0.2718336,0.00007929433,0.0001147114,0.000009747071,0.000131915,0.7208614,0.0002667958,0.006011125,0.00006794275,0.0001123104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4325638,0.00002701736,0.5656159,0.0002976896,0.0002456814,0.00066921,9.672065e-7,0.00004873149,0.0005309266],"genre_scores_gemma":[0.9790226,0.000003554704,0.02041491,0.0000532493,0.000258492,0.00006755946,0.0001488449,0.00002107861,0.000009687239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5464588,"threshold_uncertainty_score":0.2960571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09283159894633712,"score_gpt":0.3529759573499964,"score_spread":0.2601443584036593,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}