{"id":"W4311082431","doi":"10.4212/cjhp.3302","title":"Effects of a Computerized Prescriber Order Entry System on Pharmacist Prescribing","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Hospital Pharmacy","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Health Services","funders":"","keywords":"Order entry; Medicine; Pharmacist; Medical prescription; Discontinuation; Clinical pharmacy; Schedule; Computerized physician order entry; Clinical decision support system; Family medicine; Medical emergency; Emergency medicine; Nursing; Pharmacy; Decision support system; Health care; Internal medicine; Data mining; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.006421558,0.0003218269,0.0003145045,0.001366918,0.0005485498,0.002163666,0.001179277,0.0009267635,0.002919198],"category_scores_gemma":[0.05826423,0.0003212292,0.0007896643,0.001936226,0.0005264575,0.00109309,0.0009490747,0.0007346775,0.0002836334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008009516,"about_ca_system_score_gemma":0.007701516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09342374,"about_ca_topic_score_gemma":0.09286115,"domain_scores_codex":[0.9877025,0.004834716,0.001411832,0.0006684492,0.00454254,0.0008399057],"domain_scores_gemma":[0.9438573,0.02968574,0.01528574,0.002151752,0.005837087,0.003182434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01204545,0.005276156,0.7636293,0.001056365,0.000433435,0.0005412626,0.001426369,0.003294143,0.002079971,0.0003233654,0.003031158,0.2068631],"study_design_scores_gemma":[0.0006496399,0.005461362,0.9860805,0.0002552352,0.0002027953,0.0002239892,0.0004140006,0.003555185,0.001007206,0.000049626,0.002059396,0.00004104977],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954104,0.0006848339,0.0002451502,0.000634731,0.00005983598,0.0002907333,0.0003568279,0.00009531582,0.002222208],"genre_scores_gemma":[0.9970912,0.0003137559,0.001000767,0.000315953,0.00006078911,0.00009869567,0.0004344718,0.00001058096,0.0006738399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09342374,"threshold_uncertainty_score":0.1857599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03798272094243053,"score_gpt":0.3602056323732168,"score_spread":0.3222229114307862,"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."}}