{"id":"W2589984530","doi":"10.2196/medinform.6928","title":"Progress in the Enhanced Use of Electronic Medical Records: Data From the Ontario Experience","year":2017,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Medical Association; Dystonia Medical Research Foundation Canada","funders":"","keywords":"Medical record; Data collection; Medicine; Maturity (psychological); Scale (ratio); Descriptive statistics; Univariate; Multivariate analysis; Family medicine; Psychology; Multivariate statistics; Geography; Computer science; Cartography; Surgery; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004124846,0.000185442,0.0002914106,0.001439505,0.001948072,0.001409831,0.0007715002,0.0003995952,0.00168545],"category_scores_gemma":[0.01687784,0.0003374374,0.0003545161,0.004756522,0.001302475,0.00118349,0.001671531,0.0005329413,0.0001964391],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02645311,"about_ca_system_score_gemma":0.03762416,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9677716,"about_ca_topic_score_gemma":0.9831303,"domain_scores_codex":[0.9959425,0.0005312577,0.0003537515,0.0002377481,0.002249175,0.0006854497],"domain_scores_gemma":[0.9785824,0.003868895,0.005957421,0.0008163645,0.007979121,0.002795815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008662597,0.00007249394,0.9458457,0.00009761538,0.00002158357,0.0001643671,0.0379687,0.00008790676,0.0002023789,0.0001292102,0.001089925,0.01423353],"study_design_scores_gemma":[0.00000614561,0.00005031911,0.9865139,0.00004518295,0.000008437901,0.0000461387,0.01052209,0.00010917,0.00004816658,0.00001077011,0.002629924,0.000009778621],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962317,0.0002692253,0.00007365827,0.0005002382,0.000003185209,0.00004557745,0.0007273105,0.000004233129,0.002144761],"genre_scores_gemma":[0.9972184,0.0006016281,0.0002532157,0.0001313811,0.00000640452,0.00003443173,0.0006059066,0.00000638905,0.001142246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9735469,"threshold_uncertainty_score":0.1919316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1605882146243756,"score_gpt":0.4922283722707599,"score_spread":0.3316401576463843,"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."}}