{"id":"W4388042304","doi":"10.1136/jitc-2023-sitc2023.1257","title":"1257 Augmenting post-market surveillance of serious drug-induced adverse events with artificial intelligence (AI)-aggregated case reports: proof-of-concept for PD-1/PD-L1 inhibitors for NSCLC","year":2023,"lang":"en","type":"article","venue":"Regular and Young Investigator Award Abstracts","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Adverse Event Reporting System; Adverse effect; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001510589,0.0003998339,0.0005966345,0.0002403895,0.000418906,0.00001703747,0.0001476489,0.0002901439,0.00002341998],"category_scores_gemma":[0.0007688866,0.0003793573,0.0002216299,0.0005411243,0.0004211494,0.0003586297,0.00006656213,0.0004517331,0.000002641842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007173842,"about_ca_system_score_gemma":0.0005428387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001214768,"about_ca_topic_score_gemma":0.0001200666,"domain_scores_codex":[0.9971586,0.0001788139,0.001089675,0.0006244236,0.0002720229,0.0006764786],"domain_scores_gemma":[0.9972434,0.0005092259,0.0009247466,0.0003069393,0.0005124306,0.0005032411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006527644,0.001825802,0.03540868,0.004261783,0.003051681,0.006015902,0.009203478,0.03182975,0.8417797,0.0005337605,0.01608716,0.04347466],"study_design_scores_gemma":[0.00130375,0.0003056639,0.001601399,0.0002942792,0.000379543,0.001220874,0.001876114,0.005826808,0.978279,0.0008518256,0.007477964,0.0005827657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944942,0.0001508344,0.0001506628,0.0009542835,0.001466757,0.001972285,0.0005638103,0.000140156,0.0001070396],"genre_scores_gemma":[0.9981943,0.00003169395,0.0003505618,0.0002947884,0.0002557675,0.0002436417,0.0002135401,0.0000617991,0.0003539203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1364993,"threshold_uncertainty_score":0.9998658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06243865175087545,"score_gpt":0.3720942539833658,"score_spread":0.3096556022324903,"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."}}