{"id":"W6920756983","doi":"10.6084/m9.figshare.26558696.v1","title":"Additional file 2 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":"Medical Coding and Health Information","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001834798,0.0001310535,0.0004175366,0.0004046065,0.0002034591,0.00001570451,0.0001974623,0.0001960456,0.7528912],"category_scores_gemma":[0.01161938,0.0001165397,0.00006304112,0.0006507668,0.00002842983,0.0002302293,0.0001308035,0.001045499,0.00003577944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007598028,"about_ca_system_score_gemma":0.003614582,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002948837,"about_ca_topic_score_gemma":0.04998742,"domain_scores_codex":[0.9969994,0.0003026736,0.001234205,0.0004568896,0.0005128231,0.0004939364],"domain_scores_gemma":[0.9903777,0.008592747,0.0004312398,0.0002320703,0.0001812093,0.0001850067],"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":[0.00005947243,0.00004701132,0.006152927,0.003091231,0.00007055676,8.468415e-7,0.0008745075,5.879492e-7,8.810694e-8,0.001210966,0.9764147,0.01207714],"study_design_scores_gemma":[0.0004474795,0.0003365333,0.5925617,0.009173289,0.000005789951,3.045298e-7,0.0009793517,0.02232704,1.862018e-7,0.001059616,0.3729686,0.0001400197],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000809675,0.000299497,0.00000644636,0.001750979,0.00004884102,0.0006419646,0.9950675,0.00003821021,0.001336844],"genre_scores_gemma":[0.03667643,0.000139481,0.0003259687,0.0005745863,0.0002871616,0.003430997,0.958223,0.00001127486,0.0003311446],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7528555,"threshold_uncertainty_score":0.9967062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4896520268998478,"score_gpt":0.5387866716716496,"score_spread":0.04913464477180179,"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."}}