{"id":"W4408150416","doi":"10.2196/63740","title":"Linking Electronic Health Record Prescribing Data and Pharmacy Dispensing Records to Identify Patient-Level Factors Associated With Psychotropic Medication Receipt: Retrospective Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Center for Advancing Translational Sciences; National Institute of Allergy and Infectious Diseases","keywords":"Medical prescription; Medicine; Pharmacy; Retrospective cohort study; Pharmacoepidemiology; Electronic prescribing; Pharmacist; Polypharmacy; Family medicine; Medical emergency; Pediatrics; Emergency medicine; Intensive care medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004848779,0.0004104254,0.0003760703,0.003062584,0.0007740927,0.001000115,0.0008435011,0.0005681111,0.001372484],"category_scores_gemma":[0.01347902,0.0008243274,0.0008148281,0.004306657,0.0005272165,0.001122122,0.001355618,0.0008517271,0.000435538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006789,"about_ca_system_score_gemma":0.001818757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01119691,"about_ca_topic_score_gemma":0.01224107,"domain_scores_codex":[0.9935517,0.001686116,0.001460502,0.001191996,0.001601513,0.0005081495],"domain_scores_gemma":[0.9803565,0.004357359,0.009236079,0.003008919,0.002273933,0.0007671893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003030526,0.0000460978,0.9986817,0.00001690789,0.00005072576,0.00004853636,0.0000812136,0.00003109012,0.00004190473,0.00002215299,0.0001182568,0.0008311195],"study_design_scores_gemma":[0.00002209472,0.0002761871,0.995455,0.00007488747,0.000144717,0.0007370649,0.0007430577,0.0009268772,0.0002965215,0.00004073558,0.001266958,0.00001578593],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955244,0.0004224049,0.00100627,0.00005905428,0.00001227938,0.0001328094,0.002227193,0.00001235632,0.0006033851],"genre_scores_gemma":[0.9962826,0.0002915898,0.001091336,0.00009583255,0.00002449231,0.0001241783,0.001942896,0.000008978188,0.000138105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01119691,"threshold_uncertainty_score":0.02564311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1230823169063933,"score_gpt":0.4840907657122831,"score_spread":0.3610084488058898,"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."}}