{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06349745,0.0004885193,0.0005980133,0.003952185,0.002226667,0.004400096,0.001977223,0.002166655,0.001168003],"category_scores_gemma":[0.1076895,0.0004229138,0.001659424,0.006431003,0.001483793,0.003246717,0.002642778,0.001517769,0.0002495526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004814018,"about_ca_system_score_gemma":0.008545094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009392311,"about_ca_topic_score_gemma":0.006391139,"domain_scores_codex":[0.9495268,0.03418137,0.005220328,0.002220739,0.007712805,0.001137889],"domain_scores_gemma":[0.8287158,0.1233855,0.0147206,0.01124309,0.01992411,0.002010876],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009424423,0.003284602,0.4032405,0.003573047,0.0004839794,0.01007315,0.01797973,0.07202672,0.004150709,0.02410597,0.008346209,0.451793],"study_design_scores_gemma":[0.0007933829,0.002931361,0.1500212,0.002454545,0.0007440196,0.008862994,0.03028905,0.7000543,0.02272061,0.0274237,0.05324139,0.0004633898],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7123589,0.00224156,0.2624711,0.01075649,0.0001295314,0.004737451,0.001459096,0.0005881031,0.005257842],"genre_scores_gemma":[0.7978426,0.0007629332,0.1986768,0.0003580416,0.00004959745,0.000972559,0.0008557081,0.00004101684,0.000440746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9365026,"threshold_uncertainty_score":0.3358105,"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."}}