{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003416152,0.00008420237,0.000561626,0.0001884453,0.00005942144,0.000006094829,0.000111109,0.000037898,0.000006528667],"category_scores_gemma":[0.000762706,0.00004410534,0.0002078432,0.0002855082,0.0002875142,0.0001136532,0.00009175863,0.00007243974,3.695395e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007845806,"about_ca_system_score_gemma":0.0002606706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001736809,"about_ca_topic_score_gemma":0.000004670276,"domain_scores_codex":[0.9990544,0.0001762019,0.0003266416,0.00009502867,0.0002115439,0.0001361573],"domain_scores_gemma":[0.9987562,0.0001497997,0.0004769801,0.0001501208,0.0002529648,0.000213969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008350248,0.000003844447,0.9816353,0.00001189106,0.0002971578,0.0000269676,0.00003524516,0.001569499,0.007025857,0.00004116738,0.002861509,0.005656528],"study_design_scores_gemma":[0.003353881,0.001165828,0.9687235,0.0002274463,0.0004583807,0.001703106,0.0006004604,0.0003228117,0.002447164,0.0002572591,0.02049308,0.0002470285],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9638104,0.00005799644,0.02734182,0.007327641,0.0009465041,0.0001571906,0.00001756999,0.000004203365,0.0003366951],"genre_scores_gemma":[0.9979679,0.00007573665,0.001142933,0.0002511132,0.0005329528,0.000001216328,3.072202e-7,0.00001092275,0.00001692529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03415752,"threshold_uncertainty_score":0.1798563,"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."}}