{"id":"W2296273445","doi":"","title":"Quality of care metric reporting from clinical narratives: Assessing ontology components","year":2014,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Metric (unit); Ontology; Health care; Quality (philosophy); Computer science; Data extraction; Data science; Quality management; Data quality; Knowledge management; Information retrieval; Data mining; MEDLINE; Business; Operations management; Engineering; Political science; Management system; Marketing","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":[],"consensus_categories":[],"category_scores_codex":[0.001203011,0.0001135227,0.0004550949,0.00003632688,0.00005546463,0.00001155389,0.0001800848,0.0003064338,0.0000243101],"category_scores_gemma":[0.00506481,0.00008932396,0.0001633431,0.00008790457,0.000269514,0.00000208802,0.0001258947,0.0001284766,0.000002865898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006957629,"about_ca_system_score_gemma":0.00005002834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002538745,"about_ca_topic_score_gemma":0.0001306143,"domain_scores_codex":[0.9976031,0.0004830071,0.001209514,0.0003818672,0.0001482117,0.0001743091],"domain_scores_gemma":[0.9982171,0.0002369987,0.0009729171,0.0003614445,0.0001341421,0.00007740778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008322833,0.0001315321,0.662843,0.0000450922,0.0001341258,0.000002480968,0.0003267052,0.000002313748,0.1741029,0.0001340047,0.0005142488,0.1616803],"study_design_scores_gemma":[0.001107347,0.0006073532,0.9284964,0.00003837048,0.00002149958,0.000004427268,0.005043412,0.00006233351,0.04328848,0.0004545484,0.02059315,0.0002827244],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663293,0.0006498409,0.02694083,0.0001117937,0.0002660737,0.00004682836,0.000005579318,0.00002271861,0.005627043],"genre_scores_gemma":[0.9685704,0.0000160848,0.03087404,0.0001657337,0.0001768968,0.000002569831,0.0001241559,0.000007476731,0.00006266863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2656533,"threshold_uncertainty_score":0.6063417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1336655732483018,"score_gpt":0.4496565336896625,"score_spread":0.3159909604413608,"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."}}