{"id":"W4415793817","doi":"10.1161/circ.152.suppl_3.4368538","title":"Abstract 4368538: Fusion Machine Learning Architectures for Improving ECG classification of acute coronary events","year":2025,"lang":"en","type":"article","venue":"Circulation","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thornhill Medical (Canada)","funders":"","keywords":"Random forest; Deep learning; Acute coronary syndrome; Artificial neural network; Test set; Myocardial infarction; Tree (set theory); Decision tree","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00172584,0.0009639597,0.0005387484,0.0007191104,0.0001714829,0.000496664,0.0008335853,0.0006880757,0.002345479],"category_scores_gemma":[0.00319995,0.0002060827,0.0006982227,0.0005698131,0.0001749784,0.001055611,0.0008109562,0.0008663998,0.0008061401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005127085,"about_ca_system_score_gemma":0.0005696119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003727606,"about_ca_topic_score_gemma":0.00375685,"domain_scores_codex":[0.9995654,0.0001208245,0.00002941035,0.0001242085,0.0001016316,0.00005847675],"domain_scores_gemma":[0.9993659,0.0001914611,0.00007674358,0.00008524858,0.0002392658,0.00004145593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001185011,0.0006862964,0.02470995,0.0001556987,0.0004431221,0.0001316827,0.0000828899,0.2410255,0.01544316,0.001658027,0.01292847,0.7015501],"study_design_scores_gemma":[0.00004136271,0.0003342403,0.006631191,0.00003070467,0.00008726666,0.00007068364,0.00001262671,0.9848292,0.004317197,0.00238304,0.001243802,0.00001868324],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5010317,0.005129181,0.4769612,0.002103505,0.0005126941,0.000194672,0.002078924,0.005960552,0.00602755],"genre_scores_gemma":[0.9312245,0.0004673774,0.06381717,0.0002400696,0.0001485451,0.00006816836,0.001907898,0.00008028804,0.002046081],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003727606,"threshold_uncertainty_score":0.0091272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02009311199946083,"score_gpt":0.3048337090431422,"score_spread":0.2847405970436814,"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."}}