{"id":"W4411231795","doi":"10.1101/2025.06.11.25329295","title":"AI-based identification of patients who benefit from revascularization: a multicenter study","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Calgary","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; Dr. Miriam and Sheldon G. Adelson Medical Research Foundation","keywords":"Identification (biology); Revascularization; Multicenter study; Medicine; Internal medicine; Intensive care medicine; Biology; Randomized controlled trial; Myocardial infarction","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":[0.02188937,0.0009532851,0.0009129507,0.0008714201,0.000761094,0.001524535,0.00171398,0.001426458,0.001665174],"category_scores_gemma":[0.03873603,0.0005660391,0.002199108,0.001005567,0.001238977,0.001571002,0.001489686,0.001701241,0.0003896937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000917403,"about_ca_system_score_gemma":0.001173205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001829755,"about_ca_topic_score_gemma":0.001083304,"domain_scores_codex":[0.98945,0.007473095,0.0005076824,0.001755214,0.0004350801,0.000378847],"domain_scores_gemma":[0.9672773,0.01251147,0.009622214,0.006512678,0.001591503,0.002484822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01105849,0.002412501,0.9708611,0.00009604097,0.002000826,0.0001855846,0.0005328064,0.001486554,0.0004028569,0.0007201912,0.00139726,0.008845787],"study_design_scores_gemma":[0.00784801,0.01073582,0.9328867,0.0001465564,0.003716447,0.001155748,0.001139527,0.03619242,0.000555597,0.002728586,0.002747421,0.0001470218],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967193,0.0003710632,0.001563498,0.0002919474,0.00002035087,0.0001687248,0.0004259322,0.00001622479,0.0004230035],"genre_scores_gemma":[0.9977078,0.00008152438,0.001072102,0.000159671,0.00006317139,0.0001658392,0.0006732151,0.000009424991,0.00006718912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02188937,"threshold_uncertainty_score":0.1157635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07258895202100517,"score_gpt":0.3956527611114768,"score_spread":0.3230638090904716,"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."}}