{"id":"W4410819442","doi":"10.2196/65585","title":"Explainable Machine Learning Framework for Dynamic Monitoring of Disease Prognostic Risk: Retrospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Computer science; Medicine; Machine learning; Artificial intelligence; World Wide Web","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":[0.01902029,0.0008293505,0.0006895333,0.002457313,0.0004752965,0.001065213,0.00142881,0.000863791,0.001253159],"category_scores_gemma":[0.02697432,0.0004045654,0.00150169,0.001160122,0.000422879,0.0006457655,0.000929527,0.001209218,0.0002457345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006639801,"about_ca_system_score_gemma":0.0008180984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0104971,"about_ca_topic_score_gemma":0.007907363,"domain_scores_codex":[0.9968442,0.001918468,0.0002220458,0.0006607242,0.0002099379,0.0001446034],"domain_scores_gemma":[0.9828482,0.01059581,0.002374771,0.002807463,0.0009789512,0.0003948563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004398123,0.00020125,0.9442082,0.00005214352,0.0007195387,0.000456511,0.0002751472,0.02991481,0.0003330875,0.00146338,0.001313564,0.02062265],"study_design_scores_gemma":[0.00008886348,0.0005180393,0.2125842,0.00008992589,0.0005861909,0.0007298742,0.0003929821,0.7754864,0.0004320287,0.006644931,0.002378016,0.00006850916],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8741369,0.0009777388,0.1194583,0.0006704914,0.00007626159,0.0002792882,0.003720538,0.0001664542,0.0005140468],"genre_scores_gemma":[0.9775508,0.0001992388,0.01990289,0.00006364252,0.00004783194,0.0001866134,0.001843056,0.00001388327,0.0001921554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01902029,"threshold_uncertainty_score":0.1005901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02859592595268898,"score_gpt":0.4249387684061942,"score_spread":0.3963428424535052,"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."}}