{"id":"W2789433208","doi":"10.1016/j.resuscitation.2018.02.010","title":"Automated video surveillance and machine learning: Leveraging existing infrastructure for cardiac arrest detection and emergency response activation","year":2018,"lang":"en","type":"letter","venue":"Resuscitation","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Alexandra Hospital; University of Alberta","funders":"","keywords":"Medicine; Medical emergency; Terrorism; Beijing; Artificial intelligence; Computer science; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001072858,0.0003733546,0.0005145688,0.000427641,0.0004767893,0.00009225963,0.00004691979,0.0006543806,0.000006899793],"category_scores_gemma":[0.002506986,0.0003620145,0.0001416918,0.0003425828,0.0001056003,0.0002159971,0.00003922907,0.0008349242,0.000003230876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000216593,"about_ca_system_score_gemma":0.00009790917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006342832,"about_ca_topic_score_gemma":0.00001248433,"domain_scores_codex":[0.9975744,0.0004949927,0.0004883303,0.0006748265,0.0004165624,0.0003509029],"domain_scores_gemma":[0.9978632,0.0008228038,0.0004792412,0.0002287808,0.0005165017,0.00008946559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.01167259,0.00004010095,0.0956075,0.007579069,0.001184727,0.00006253657,0.006692992,0.0004162882,0.2714396,0.00001688653,0.5588834,0.04640432],"study_design_scores_gemma":[0.001942568,0.0003716268,0.6113253,0.0006011463,0.0002918036,0.00001145252,0.0002566552,0.0267489,0.001091372,0.0003242251,0.3563621,0.0006728527],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9130701,0.0007132751,0.005995525,0.069052,0.007485158,0.002427505,0.0001385456,0.0008491297,0.0002687748],"genre_scores_gemma":[0.9803421,0.00043553,0.001785577,0.004543148,0.009133135,0.0002014595,0.002668516,0.0001897303,0.0007008204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5157178,"threshold_uncertainty_score":0.9998832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01998424528879153,"score_gpt":0.2848154483688827,"score_spread":0.2648312030800911,"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."}}