{"id":"W3005333799","doi":"10.1016/j.cjca.2020.01.027","title":"Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Instituto de Salud Carlos III; Centro de Investigación Biomédica en Red Enfermedades Cardiovasculares; Ministerio de Ciencia, Innovación y Universidades","keywords":"Medicine; Restenosis; Stent; Internal medicine; Cardiology","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":[0.001634818,0.0005123428,0.0007131949,0.001129725,0.000237333,0.0006693926,0.0004509971,0.0006455683,0.001175809],"category_scores_gemma":[0.006119506,0.0001427854,0.0004858808,0.0006780047,0.0001747363,0.0005800939,0.0002654342,0.0008636506,0.0005227323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002967044,"about_ca_system_score_gemma":0.0004066002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002277344,"about_ca_topic_score_gemma":0.00273887,"domain_scores_codex":[0.9994488,0.0002081072,0.00006664523,0.0001314866,0.00007546227,0.00006953083],"domain_scores_gemma":[0.9960698,0.002557036,0.0005025631,0.0002695782,0.0003564313,0.0002446671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001065049,0.001145209,0.9101343,0.00003766162,0.000322002,0.00008932441,0.00005292872,0.01028201,0.0009877693,0.0001695172,0.002113503,0.07360076],"study_design_scores_gemma":[0.0001056722,0.001032894,0.519864,0.00004383726,0.0001971235,0.0004609639,0.0001442204,0.4743583,0.0009467818,0.002062337,0.0007431478,0.00004076794],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916664,0.0004797692,0.005513777,0.0004118629,0.0000697421,0.00003043928,0.0009596305,0.0001104818,0.0007580277],"genre_scores_gemma":[0.9955478,0.0001340852,0.002521252,0.00006119334,0.00007393838,0.00002398878,0.001248683,0.000008304252,0.0003806782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002277344,"threshold_uncertainty_score":0.008645892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03317942063612791,"score_gpt":0.2777991841531725,"score_spread":0.2446197635170446,"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."}}