{"id":"W2802392436","doi":"10.3389/fphar.2018.00435","title":"A Semantic Transformation Methodology for the Secondary Use of Observational Healthcare Data in Postmarketing Safety Studies","year":2018,"lang":"en","type":"article","venue":"Frontiers in Pharmacology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Seventh Framework Programme","keywords":"Observational study; Health care; Medicine; Postmarketing surveillance; Patient safety; Data science; Computer science; Adverse effect; Pharmacology; Risk analysis (engineering); Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02247511,0.0008829487,0.0007552074,0.008220059,0.001448081,0.005129588,0.002373872,0.001470396,0.002595632],"category_scores_gemma":[0.02287837,0.0008459038,0.004226762,0.007101601,0.002599866,0.007569228,0.007548723,0.002906568,0.001344827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002001266,"about_ca_system_score_gemma":0.007204411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003693249,"about_ca_topic_score_gemma":0.004422554,"domain_scores_codex":[0.9825827,0.006729449,0.003024664,0.002224101,0.005023573,0.0004156017],"domain_scores_gemma":[0.9833745,0.006763681,0.001408083,0.004427397,0.003620681,0.0004056471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001862276,0.0005245084,0.006765782,0.00120707,0.0003782041,0.001336462,0.00618919,0.01991788,0.01426115,0.5999122,0.009467276,0.3398541],"study_design_scores_gemma":[0.0001213118,0.000232806,0.006793156,0.0009850272,0.0003464739,0.001580499,0.003953997,0.1921026,0.0409405,0.5006601,0.2520306,0.0002530027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001821955,0.00004723322,0.9948663,0.0002639681,0.00002936001,0.0003359999,0.0004264951,0.001016411,0.001192398],"genre_scores_gemma":[0.02970674,0.0001436258,0.9660696,0.0001867105,0.00003298089,0.0005977267,0.002293799,0.0002647885,0.0007038799],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02247511,"threshold_uncertainty_score":0.1188611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3042727212310511,"score_gpt":0.4492167026569809,"score_spread":0.1449439814259298,"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."}}