{"id":"W4416179655","doi":"10.2196/73884","title":"Process for Quality Management of Electronic Medical Records–Based Data: Case Study Using Real Colorectal Cancer Data","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Quality management; Missing data; Quality (philosophy); Data quality; Colorectal cancer; Data collection; Data management","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":[0.004756607,0.0001910237,0.0004361383,0.0001418714,0.0001873644,0.00005691799,0.004473746,0.0001878554,0.00004419159],"category_scores_gemma":[0.0006270854,0.0001631824,0.00003489511,0.0007465507,0.000119648,0.0006527197,0.002408572,0.0006565442,0.000001212955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001815325,"about_ca_system_score_gemma":0.003330544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002172371,"about_ca_topic_score_gemma":0.002254753,"domain_scores_codex":[0.9957701,0.0002327549,0.001365949,0.0004014135,0.001702767,0.0005270059],"domain_scores_gemma":[0.996547,0.0005607606,0.000442141,0.001962322,0.0002099338,0.0002778697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004962558,0.003771213,0.08312529,0.03494084,0.001251183,0.001275667,0.02120375,0.001241651,0.000001312971,0.0198054,0.008987019,0.8239004],"study_design_scores_gemma":[0.001417222,0.0001923531,0.0008206799,0.0004226819,0.0000456438,0.00005612657,0.003461348,0.9922664,0.000004805428,0.0001121117,0.001041742,0.0001588604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7202257,0.00006184279,0.2749842,0.001610934,0.0005232309,0.002016631,0.0001123059,0.0001539037,0.0003113176],"genre_scores_gemma":[0.9807078,0.00004721829,0.01775556,0.0008980505,0.00009820082,0.0002522832,0.0002054227,0.00001265951,0.00002276597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9910248,"threshold_uncertainty_score":0.8313408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1179922915190361,"score_gpt":0.5043278933597475,"score_spread":0.3863356018407114,"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."}}