{"id":"W2188609437","doi":"","title":"Using EMRs to fuel quality improvement.","year":2015,"lang":"en","type":"article","venue":"PubMed","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Alberta Hospital; University of Alberta; North York General Hospital","funders":"","keywords":"Medical record; Quality management; Data science; Primary care; Electronic medical record; Computer science; Quality (philosophy); Health care; Health records; Medicine; MEDLINE; Medical emergency; Family medicine; Operations management; Engineering","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":[0.06237797,0.001179079,0.0008327656,0.007392772,0.001746129,0.006933977,0.002805766,0.004032861,0.02648121],"category_scores_gemma":[0.2292294,0.0005563184,0.00119694,0.007942904,0.00168119,0.008428322,0.008162998,0.005156361,0.008414438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00424087,"about_ca_system_score_gemma":0.01743321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007684011,"about_ca_topic_score_gemma":0.00904792,"domain_scores_codex":[0.9271773,0.03940401,0.005406119,0.003191795,0.0232823,0.001538393],"domain_scores_gemma":[0.7327264,0.1370467,0.03269548,0.0227387,0.05776368,0.01702899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001569823,0.0002014028,0.009157677,0.003035497,0.0001883307,0.0001114421,0.0006954991,0.0004343152,0.0005130128,0.01941587,0.4083559,0.5577341],"study_design_scores_gemma":[0.000118048,0.0003482453,0.0145725,0.01043775,0.0001784717,0.0002803529,0.00114466,0.0009293355,0.001549626,0.01373911,0.9566252,0.00007667099],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.01113218,0.08653745,0.03808719,0.725934,0.02144741,0.000706813,0.009594994,0.002527032,0.104033],"genre_scores_gemma":[0.2398402,0.1406164,0.2136284,0.3064491,0.04291208,0.001426539,0.01723219,0.001168254,0.03672693],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06237797,"threshold_uncertainty_score":0.3298902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7310486292711144,"score_gpt":0.5456673179983532,"score_spread":0.1853813112727611,"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."}}