{"id":"W4410318415","doi":"10.2196/67513","title":"Predicting Drug–Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Computer science; Parametric statistics; Drug; Medicine; Pharmacology; World Wide Web; Mathematics; Statistics","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":[0.002021642,0.0007201182,0.0002837505,0.001141362,0.0002631763,0.0007910561,0.000690523,0.0005411508,0.001412343],"category_scores_gemma":[0.007338223,0.0003321692,0.000919742,0.0007454324,0.0004218688,0.001469151,0.000719354,0.001080957,0.0003682778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009651226,"about_ca_system_score_gemma":0.00120483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006643334,"about_ca_topic_score_gemma":0.01023897,"domain_scores_codex":[0.9991928,0.0004222941,0.00005477139,0.0002052781,0.00008637136,0.00003847879],"domain_scores_gemma":[0.993526,0.005513735,0.0002762366,0.0003313629,0.0002934702,0.00005922619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008174778,0.0007631101,0.05114522,0.0005001926,0.0004122408,0.0008042753,0.0005184645,0.5044197,0.01149003,0.008925677,0.004183818,0.4160198],"study_design_scores_gemma":[0.00001786346,0.0001077822,0.003275831,0.0000204459,0.00005818143,0.0001145924,0.00008935325,0.9878759,0.002644438,0.004826235,0.0009519631,0.00001736568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4320177,0.001139258,0.5581483,0.001066486,0.00005632532,0.0002494997,0.002484651,0.001621529,0.003216269],"genre_scores_gemma":[0.8576627,0.0003185273,0.1379564,0.0001319632,0.00002776674,0.0001747072,0.002705082,0.00004777655,0.0009750813],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006643334,"threshold_uncertainty_score":0.01320934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1504442496576845,"score_gpt":0.4857922108775,"score_spread":0.3353479612198155,"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."}}