{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002186273,0.0004328435,0.0004132267,0.001295537,0.0003798991,0.0008222085,0.0006376684,0.0005442322,0.001611176],"category_scores_gemma":[0.007008619,0.0002463204,0.0009151084,0.0008268641,0.0001772381,0.000607446,0.00036954,0.0008172226,0.0003808638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004047744,"about_ca_system_score_gemma":0.0004796108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002586045,"about_ca_topic_score_gemma":0.00186175,"domain_scores_codex":[0.9992525,0.0002316687,0.0001036549,0.0002065767,0.0001252091,0.00008035498],"domain_scores_gemma":[0.9971598,0.00109242,0.0005673997,0.0003472375,0.000575275,0.0002578197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002659728,0.0001427254,0.9966024,0.0000135659,0.00008833297,0.00007574833,0.00004634719,0.000357762,0.00008279057,0.00002930329,0.0001946866,0.002100299],"study_design_scores_gemma":[0.0001026186,0.0008670226,0.9720071,0.0000287612,0.0002426575,0.0007555367,0.0004375817,0.02444946,0.0002258015,0.0002117869,0.0006493061,0.00002225136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980211,0.0001163916,0.0008997776,0.0000472375,0.00001316977,0.00008840516,0.0005599053,0.00001296557,0.0002410129],"genre_scores_gemma":[0.9974208,0.0000698173,0.001046687,0.00002017459,0.00001331225,0.00009003119,0.001155391,0.000004621452,0.0001792074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002586045,"threshold_uncertainty_score":0.01156223,"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."}}