{"id":"W2974623409","doi":"10.1016/j.cjca.2019.09.007","title":"Machine Intelligence for Management of Acute Coronary Syndromes: Neural or Nervous Times?","year":2019,"lang":"en","type":"letter","venue":"Canadian Journal of Cardiology","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Medicine; Artificial neural network; Cardiology; Intensive care medicine; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002538708,0.0003639114,0.001070013,0.000689862,0.001368808,0.002766245,0.001112391,0.01513989,0.005200778],"category_scores_gemma":[0.02554065,0.0002265434,0.0007166908,0.000478786,0.002941586,0.003790849,0.0008828772,0.02033932,0.003001104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002509419,"about_ca_system_score_gemma":0.002535766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002357997,"about_ca_topic_score_gemma":0.004968896,"domain_scores_codex":[0.998513,0.0006092525,0.0002257687,0.0001198497,0.0003553375,0.0001767847],"domain_scores_gemma":[0.9870967,0.009382224,0.0007582994,0.0003095912,0.00132613,0.001127053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003018451,0.0001244937,0.004314319,0.0003879715,0.00009740579,0.005773154,0.0002599312,0.0005712705,0.0003475574,0.04572942,0.7837137,0.1583789],"study_design_scores_gemma":[0.0004466975,0.0002364271,0.004506385,0.002815741,0.0000918294,0.01003002,0.0009163758,0.004289028,0.0003968786,0.2956142,0.680514,0.0001423492],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0005284898,0.006398798,0.0003816405,0.9839157,0.004868024,0.00000511144,0.00001996505,0.00001467925,0.003867501],"genre_scores_gemma":[0.04721656,0.02810969,0.002061057,0.7209523,0.1951839,0.00008585385,0.00007885646,0.00003463021,0.006277142],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01513989,"threshold_uncertainty_score":0.01820719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0330746267035862,"score_gpt":0.2926928836532488,"score_spread":0.2596182569496626,"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."}}