{"id":"W4391113254","doi":"10.1136/bmjopen-2023-073455","title":"Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study","year":2024,"lang":"en","type":"article","venue":"BMJ Open","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bishop's University","funders":"British Heart Foundation","keywords":"Medicine; Protocol (science); Epidemiology; Health records; Electronic health record; Public health; Population; Heart failure; Medical emergency; Environmental health; Alternative medicine; Health care; Internal medicine; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002358199,0.0001370099,0.0001967734,0.0001241541,0.0003631612,0.0007688471,0.0004893402,0.00004175853,0.000006571632],"category_scores_gemma":[0.0002183748,0.0001296323,0.00002268775,0.0002485651,0.000003614467,0.0005650105,0.0003070542,0.0001923965,0.000004656509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003882423,"about_ca_system_score_gemma":0.002306968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003942548,"about_ca_topic_score_gemma":0.005594369,"domain_scores_codex":[0.9979652,0.0002742393,0.0005249341,0.000579688,0.0003761886,0.0002797798],"domain_scores_gemma":[0.9990332,0.000256316,0.0001791555,0.0003467978,0.0000968818,0.00008763195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009514107,0.0001590562,0.9108484,0.0008213873,0.00004413865,0.000001853207,0.007472557,0.04212497,0.000006244635,0.003556649,0.001529843,0.03333974],"study_design_scores_gemma":[0.0006766182,0.0002826043,0.06860379,0.0003537726,0.000002566288,0.000001692949,0.00007228185,0.9252204,0.0000344397,0.002155401,0.0024461,0.0001503382],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.07807739,0.000008621807,0.4389394,0.01262978,0.0001033029,0.4699214,0.000008529489,0.0002911118,0.00002048578],"genre_scores_gemma":[0.3523171,3.251967e-8,0.2012547,0.0004434304,0.00006067915,0.4457877,0.00005198954,0.00001872221,0.00006567061],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.8830954,"threshold_uncertainty_score":0.741401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08673745895430299,"score_gpt":0.4508447893036423,"score_spread":0.3641073303493393,"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."}}