{"id":"W2150668840","doi":"10.1016/j.ijmedinf.2005.07.037","title":"Integrating feedback from a clinical data warehouse into practice organisation","year":2005,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke","funders":"","keywords":"Data warehouse; SNOMED CT; Dashboard; Computer science; Database; Data quality; Electronic health record; Health care; Engineering; Terminology","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":[],"consensus_categories":[],"category_scores_codex":[0.02004101,0.001080143,0.001042789,0.00343688,0.0008504729,0.006790353,0.001332434,0.001340178,0.00340695],"category_scores_gemma":[0.1007506,0.0007398602,0.0006600516,0.002981425,0.0003477792,0.00443622,0.002206625,0.001342439,0.002018856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001665071,"about_ca_system_score_gemma":0.003466053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005610784,"about_ca_topic_score_gemma":0.007162325,"domain_scores_codex":[0.9840005,0.007857841,0.002289848,0.001130417,0.004318688,0.0004026876],"domain_scores_gemma":[0.8417898,0.09697174,0.007900532,0.01332738,0.03665986,0.00335074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003966676,0.003622206,0.1509769,0.001938914,0.0003784099,0.001069075,0.01984123,0.0110129,0.02603646,0.001362554,0.03543829,0.7443565],"study_design_scores_gemma":[0.001914105,0.01142063,0.2920244,0.003891567,0.002345203,0.001901303,0.03155036,0.3468645,0.1416287,0.01409471,0.1512728,0.001091858],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7704333,0.001001097,0.1622213,0.007723496,0.001033972,0.00217078,0.01303175,0.02934283,0.01304155],"genre_scores_gemma":[0.7900096,0.0005806367,0.1885806,0.000768055,0.0002596698,0.0005273246,0.01343513,0.001247055,0.004591953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02004101,"threshold_uncertainty_score":0.1059882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1627002221237205,"score_gpt":0.5695588192799976,"score_spread":0.4068585971562771,"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."}}