{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006713651,0.000379025,0.001307733,0.0007187952,0.00009966722,0.00005515749,0.002922245,0.0005920821,0.00005185089],"category_scores_gemma":[0.00007471244,0.0003118261,0.0005287827,0.0002782528,0.0001581757,0.0001577244,0.0001559043,0.001948578,0.00002082288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000308917,"about_ca_system_score_gemma":0.001668076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006322169,"about_ca_topic_score_gemma":0.0001130753,"domain_scores_codex":[0.996968,0.0004388395,0.00100117,0.000464522,0.0003150035,0.000812515],"domain_scores_gemma":[0.9968672,0.0005028755,0.0009541861,0.0009231,0.0004365293,0.00031609],"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":[0.00006352021,0.000001511982,0.001652195,0.001128714,0.001757876,0.01774047,0.000150039,0.0102856,6.031732e-7,0.001063555,0.9135095,0.05264641],"study_design_scores_gemma":[0.0003352639,0.001603834,0.001825728,0.000337921,0.0003016976,0.01394124,0.00001685439,0.004887006,0.000002021477,0.0009124962,0.9754334,0.000402549],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0002749826,0.003982532,0.458414,0.520028,0.01231356,0.001425826,0.0005157735,0.00004064479,0.003004658],"genre_scores_gemma":[0.3073774,0.001593806,0.08457254,0.5733821,0.01179032,0.0001141367,0.0005014746,0.0004152814,0.02025297],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.3738415,"threshold_uncertainty_score":0.9999334,"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."}}