{"id":"W7105695419","doi":"10.1136/bmjdhai-2025-000134","title":"Retrospective evaluation of a machine learning model to facilitate pharmacogenetic testing","year":2025,"lang":"en","type":"article","venue":"BMJ Digital Health & AI","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; Institute for Clinical Evaluative Sciences; Hospital for Sick Children","funders":"Hospital for Sick Children","keywords":"Pharmacogenetics; Logistic regression; Medical prescription; Epidemiology; Retrospective cohort study; Cohort; Gradient boosting","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01155047,0.001085383,0.0008536279,0.001213893,0.0003214807,0.001569667,0.001169325,0.0009429645,0.001714491],"category_scores_gemma":[0.02480706,0.0005179086,0.000965496,0.0007151265,0.0003985496,0.001078783,0.0009017152,0.001307303,0.0006782897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001021221,"about_ca_system_score_gemma":0.00125393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008522661,"about_ca_topic_score_gemma":0.004628108,"domain_scores_codex":[0.9974602,0.001415024,0.0002314272,0.0004901859,0.0002686555,0.0001344815],"domain_scores_gemma":[0.9864031,0.00992786,0.0009811298,0.0007720562,0.001752733,0.0001629966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005919615,0.0002256459,0.1091787,0.0001261978,0.0003165953,0.0002942417,0.0001418954,0.8104613,0.001456026,0.002941646,0.002542356,0.07172356],"study_design_scores_gemma":[0.00001072359,0.00008206791,0.00311261,0.00002039834,0.00002492844,0.00006352697,0.00001439454,0.9949805,0.0004657035,0.0007221044,0.0004936495,0.000009354327],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3750523,0.001199893,0.6145654,0.001585803,0.0001745382,0.0003584093,0.002293455,0.001896034,0.002874231],"genre_scores_gemma":[0.9052494,0.0003182569,0.09027218,0.0002211422,0.00008834068,0.0002543739,0.00232434,0.00007009007,0.001201872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01155047,"threshold_uncertainty_score":0.06108546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3195143293878861,"score_gpt":0.5259982311863415,"score_spread":0.2064839017984554,"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."}}