{"id":"W2509058245","doi":"10.1016/j.jacc.2016.03.609","title":"Overcoming Spatial and Temporal Barriers to Public Access Defibrillators Via Optimization","year":2016,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":112,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Kingston General Hospital; St. Michael's Hospital; University of Toronto","funders":"Canadian Institutes of Health Research; ZOLL Foundation; ZOLL Medical Corporation; National Heart, Lung, and Blood Institute; Heart and Stroke Foundation of Canada; Institute of Circulatory and Respiratory Health; Laerdal Foundation for Acute Medicine; University of Toronto; National Institute of Neurological Disorders and Stroke; American Heart Association","keywords":"Medicine; Software deployment; Automated external defibrillator; Emergency medicine; Medical emergency; Population; Cohort; Cardiopulmonary resuscitation; Environmental health; Internal medicine; Resuscitation; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001000963,0.0006154276,0.0007099789,0.0005356128,0.0005684597,0.001422838,0.0007676343,0.0006492606,0.005826679],"category_scores_gemma":[0.007613727,0.0003407678,0.0003557556,0.0004830946,0.0004863171,0.001784736,0.001260607,0.0008759838,0.0003356103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008331211,"about_ca_system_score_gemma":0.001976642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007141663,"about_ca_topic_score_gemma":0.007375584,"domain_scores_codex":[0.9993241,0.0002716133,0.0000252008,0.0001042757,0.0001077629,0.0001669527],"domain_scores_gemma":[0.9968792,0.002173375,0.000339009,0.0001200827,0.0003720384,0.0001162339],"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":[0.000177451,0.0001615567,0.003036077,0.00008256106,0.0000474096,0.00006372113,0.0001119754,0.9319135,0.001501369,0.0207923,0.00363226,0.03847981],"study_design_scores_gemma":[0.00001485503,0.0000816047,0.0008617995,0.00001369985,0.00002069904,0.00001929448,0.0001660981,0.9880973,0.0003368257,0.008920075,0.001458283,0.00000948201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2844859,0.001200628,0.6508896,0.004455511,0.000300595,0.000135762,0.0002809319,0.000726686,0.05752437],"genre_scores_gemma":[0.9752166,0.0001275958,0.02178633,0.0001271123,0.00003828527,0.00003497269,0.00006001382,0.00006287577,0.002546203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007141663,"threshold_uncertainty_score":0.01949221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120318716113558,"score_gpt":0.2730214968074236,"score_spread":0.261818309646288,"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."}}