{"id":"W4388033323","doi":"10.2196/51375","title":"AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study","year":2023,"lang":"en","type":"article","venue":"JMIR Cardio","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiteit Leiden; Boston Scientific Corporation","keywords":"Medicine; Acute coronary syndrome; Emergency department; Chest pain; Overcrowding; Emergency medicine; Hyperparameter; Retrospective cohort study; Algorithm; Machine learning; Medical emergency; Myocardial infarction; Internal medicine; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0005085962,0.0002152129,0.0006528156,0.0003344587,0.0001125127,0.00002837616,0.0001243578,0.0001510251,0.00003039045],"category_scores_gemma":[0.00007902331,0.0002148828,0.000192931,0.001210784,0.00004453041,0.0001352635,0.00009513582,0.0004205349,0.0005787563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009440904,"about_ca_system_score_gemma":0.0002991468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001141069,"about_ca_topic_score_gemma":0.0000502185,"domain_scores_codex":[0.9976451,0.0001144773,0.0004479057,0.0006220429,0.0006681479,0.0005022809],"domain_scores_gemma":[0.9987227,0.0000562563,0.0000466391,0.0005814774,0.0003137437,0.0002791298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006727985,0.0001338656,0.9660655,0.00003155492,0.0002746611,0.0007033031,0.01021098,0.00002633207,0.00001701531,0.000005089123,0.007682573,0.01478186],"study_design_scores_gemma":[0.0001129125,0.00186734,0.9853194,0.0000725485,0.0001486967,0.00005967612,0.01106967,0.00007264262,0.0001159136,0.00005220282,0.0009162558,0.0001927916],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899886,0.0001549804,0.00003682076,0.001114526,0.002113601,0.005497206,0.00009815547,0.0002617954,0.0007343145],"genre_scores_gemma":[0.9960437,0.00006297394,0.00008829833,0.0002675687,0.000471797,0.001978853,0.0001622241,0.0000471446,0.0008773873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01925385,"threshold_uncertainty_score":0.8762667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03223881376019673,"score_gpt":0.3796854272043053,"score_spread":0.3474466134441085,"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."}}