{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004517646,0.0002192517,0.0004228055,0.0007251757,0.001151956,0.0001906546,0.001239997,0.00009640273,0.000009611318],"category_scores_gemma":[0.005854022,0.0002004976,0.0001102824,0.002058827,0.0001322762,0.0005824352,0.0009295112,0.002095791,0.00001337424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006287931,"about_ca_system_score_gemma":0.0003487521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003012921,"about_ca_topic_score_gemma":0.0000108901,"domain_scores_codex":[0.9953533,0.001492295,0.0005126167,0.000637107,0.001184919,0.0008197983],"domain_scores_gemma":[0.994557,0.002875285,0.0002639367,0.0008361021,0.001265958,0.0002017549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000117249,0.0003470377,0.9708081,0.0004800972,0.00007709385,0.000005454179,0.007257045,0.0007548594,0.000002526849,0.01489635,0.00003372042,0.00522044],"study_design_scores_gemma":[0.0003823179,0.001021951,0.7830426,0.0003215096,0.00001017411,3.164775e-7,0.001393326,0.1950956,0.00003013965,0.0184918,0.00008334449,0.0001268699],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6650881,0.0003374996,0.3290957,0.0004432741,0.0003353333,0.004143131,0.00002281398,0.000182537,0.0003516498],"genre_scores_gemma":[0.986917,0.00005107417,0.01034764,0.000005051113,0.00004191434,0.002166111,0.000009078726,0.00002159821,0.0004405087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.321829,"threshold_uncertainty_score":0.9105285,"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."}}