{"id":"W2998304825","doi":"10.15586/jptcp.v26i2.607","title":"Preparing for Vanessa’s Law: Collaboration between the Medical Records and Pharmacy Departments at a Canadian Hospital Center","year":2019,"lang":"en","type":"article","venue":"Journal of Population Therapeutics and Clinical Pharmacology","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Pharmacy; Medical record; Coding (social sciences); Medicine; Clinical pharmacy; Context (archaeology); Medical classification; Diagnosis code; Information center; Medical diagnosis; Medical emergency; Family medicine; Nursing; Internal medicine; Psychology","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":[0.03085473,0.0004318813,0.0004889104,0.004952949,0.01400024,0.006117322,0.003626091,0.001148123,0.002417012],"category_scores_gemma":[0.07060245,0.0009585731,0.0005035004,0.006448539,0.004406995,0.002723226,0.007028791,0.002977495,0.0002822969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.112564,"about_ca_system_score_gemma":0.2785453,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9269651,"about_ca_topic_score_gemma":0.9598682,"domain_scores_codex":[0.9488862,0.019059,0.004172246,0.004581792,0.01514814,0.008152631],"domain_scores_gemma":[0.895271,0.02415867,0.02216211,0.004468449,0.03014282,0.02379691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002413693,0.0005286111,0.6434977,0.0005402212,0.0001468627,0.005540027,0.107981,0.0006703706,0.00140173,0.003406413,0.04279415,0.1932515],"study_design_scores_gemma":[0.00007124819,0.0004990781,0.6092154,0.001975136,0.0001267577,0.003068912,0.2383785,0.002688304,0.00154348,0.00137412,0.1406612,0.0003979119],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8781591,0.006228888,0.007295456,0.07834122,0.0005908443,0.001822464,0.001227365,0.0002932306,0.02604151],"genre_scores_gemma":[0.969246,0.002351815,0.01431716,0.00887587,0.000176495,0.0002960096,0.0005717019,0.0000805909,0.004084351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.112564,"threshold_uncertainty_score":0.816713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1044829879603354,"score_gpt":0.5025750682912555,"score_spread":0.3980920803309201,"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."}}