{"id":"W3113372188","doi":"10.1177/1755738020972128","title":"Pharmacogenomics: Prescribing based on genetic variation","year":2020,"lang":"en","type":"article","venue":"InnovAiT Education and inspiration for general practice","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pharmacogenomics; Polypharmacy; Medicine; Drug; Pharmacogenetics; Drug response; Disease; Intensive care medicine; Clinical decision support system; Function (biology); Adverse effect; Clinical Practice; Decision support system; Pharmacology; Family medicine; Computer science; Data mining; Internal medicine; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005774583,0.0002293774,0.000179269,0.000139675,0.0003683096,0.0001086379,0.0001572763,0.0001748681,0.0003224462],"category_scores_gemma":[0.0006355198,0.0002466953,0.0000598439,0.0003226166,0.00004978365,0.0003767926,0.00003038472,0.0003823809,0.00007193973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000521118,"about_ca_system_score_gemma":0.0003993303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001636596,"about_ca_topic_score_gemma":6.617778e-7,"domain_scores_codex":[0.9982857,0.0003541253,0.0004749854,0.0004510444,0.0001710349,0.0002631651],"domain_scores_gemma":[0.9984902,0.0003009232,0.0003261524,0.0001340686,0.0004617721,0.0002868928],"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":[0.001691789,0.001368439,0.0005370313,0.0001383669,0.0001680883,0.000002620738,0.00242391,0.02527593,0.4444198,0.01971916,0.03095976,0.4732951],"study_design_scores_gemma":[0.001740093,0.0002125262,0.0008576045,0.000004081692,0.0001902135,0.000005441809,0.00007121284,0.2088194,0.02244233,0.0002148138,0.7651981,0.0002442703],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8027501,0.003106541,0.05632537,0.09361967,0.008164287,0.004007367,0.0003117476,0.0003585193,0.03135644],"genre_scores_gemma":[0.8154232,0.0002428117,0.01787042,0.1634764,0.002234443,0.000260908,0.0001573884,0.00004331323,0.0002911651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7342383,"threshold_uncertainty_score":0.9999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1459867145202045,"score_gpt":0.4460471017501133,"score_spread":0.3000603872299088,"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."}}