{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001510739,0.001309785,0.001088562,0.001587801,0.0005150855,0.001341902,0.001173698,0.001019763,0.002473741],"category_scores_gemma":[0.005749284,0.0008059036,0.001301504,0.001170799,0.0007865477,0.001218017,0.001253454,0.0007965398,0.0003962302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001996828,"about_ca_system_score_gemma":0.002524044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02651776,"about_ca_topic_score_gemma":0.0134362,"domain_scores_codex":[0.9994715,0.0002381702,0.00002899479,0.0001079139,0.00009159995,0.00006186866],"domain_scores_gemma":[0.997042,0.002068214,0.0004841824,0.00007952355,0.000250532,0.00007547902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008344618,0.00001038306,0.0008466053,0.00001922767,0.00001567275,0.00001377922,0.00001190861,0.9941754,0.0001154979,0.001626367,0.0001872117,0.002969564],"study_design_scores_gemma":[0.000003267241,0.000004628896,0.0001480679,0.000004132075,0.000003256587,0.000004235158,0.000009677359,0.9986501,0.00006315771,0.0009676252,0.000139376,0.000002590745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05258491,0.000268583,0.9411911,0.0006360229,0.00003398812,0.0001166523,0.0002927966,0.000365654,0.004510242],"genre_scores_gemma":[0.6850318,0.0004673638,0.3101466,0.0002049512,0.00005730241,0.0003940296,0.0004899967,0.0001765708,0.003031348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02651776,"threshold_uncertainty_score":0.05272681,"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."}}