{"id":"W2273639774","doi":"10.1093/jamia/ocv171","title":"Potential benefit of electronic pharmacy claims data to prevent medication history errors and resultant inpatient order errors","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto; University Health Network","funders":"National Center for Advancing Translational Sciences; National Institute on Aging","keywords":"Medicine; Pharmacy; Emergency medicine; Family medicine","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":[0.008391418,0.0001468329,0.0005574236,0.0001923041,0.0001765562,0.000003817043,0.0008689706,0.0001263315,0.0001192417],"category_scores_gemma":[0.006123054,0.00008301753,0.00007015973,0.0003765683,0.0001157252,0.0003454977,0.0003332223,0.0009694298,0.00001691746],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004866083,"about_ca_system_score_gemma":0.004599556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004519364,"about_ca_topic_score_gemma":0.0003198093,"domain_scores_codex":[0.9937979,0.0008040392,0.002597712,0.0001154439,0.002069032,0.0006158584],"domain_scores_gemma":[0.9899088,0.0009540031,0.007485928,0.0004655233,0.000763178,0.0004225548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001461364,0.001021044,0.1144655,0.001150528,0.001507086,0.000004970042,0.0400481,0.0001639415,0.003903461,0.001522575,0.4314307,0.4033206],"study_design_scores_gemma":[0.01189964,0.003706426,0.1190287,0.006464191,0.0008576644,0.00009867398,0.01157333,0.03662588,0.0003657834,0.001458974,0.8069437,0.000977035],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602276,0.0002235016,0.003658808,0.03337175,0.001625052,0.0006865975,0.00003645228,0.00001448711,0.0001557188],"genre_scores_gemma":[0.9922395,0.001700569,0.0007605655,0.004287213,0.0004657234,0.00002028229,0.00001081636,0.0000221423,0.0004931646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4023436,"threshold_uncertainty_score":0.9989541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03939793719492121,"score_gpt":0.3989522167793336,"score_spread":0.3595542795844123,"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."}}