{"id":"W6920979262","doi":"10.6084/m9.figshare.26558702","title":"Additional file 4 of Real world challenges in integrating electronic medical record and administrative health data for regional quality improvement in diabetes: a retrospective cross-sectional analysis","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Medical record; Quality (philosophy); Quality management; Data collection; Data quality; Electronic health record; Electronic medical record","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002667252,0.0006763567,0.0006806322,0.003119556,0.001030333,0.001021351,0.001528068,0.0007566314,0.6061009],"category_scores_gemma":[0.03219939,0.0003925762,0.0007474319,0.006259921,0.0002501338,0.001573302,0.0009906283,0.000850716,0.02863943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001867506,"about_ca_system_score_gemma":0.002832621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02435584,"about_ca_topic_score_gemma":0.02701324,"domain_scores_codex":[0.998036,0.0004292863,0.0006006213,0.0002771465,0.0004174878,0.0002395486],"domain_scores_gemma":[0.9624733,0.02293546,0.00669064,0.001915114,0.005220833,0.000764734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0006214727,0.0002329085,0.02850151,0.002216825,0.000090132,0.0001034284,0.0001868758,0.0005288554,0.0001008156,0.00102405,0.9554374,0.0109557],"study_design_scores_gemma":[0.01067615,0.001015608,0.502286,0.008293943,0.0004810393,0.001050718,0.003504219,0.005212746,0.00129491,0.006111956,0.459785,0.0002878734],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.001536477,0.00001471524,0.0002081035,0.0001318364,0.00001454254,0.0002764596,0.9964455,0.00005622474,0.001316224],"genre_scores_gemma":[0.04925706,0.0001548747,0.003802488,0.0007385942,0.0001606004,0.007993544,0.9256343,0.0002077412,0.01205078],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6061009,"threshold_uncertainty_score":0.5618493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3019029526038489,"score_gpt":0.520737766282297,"score_spread":0.2188348136784481,"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."}}