{"id":"W4405934475","doi":"10.2196/55277","title":"Creation of Scientific Response Documents for Addressing Product Medical Information Inquiries: Mixed Method Approach Using Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"JMIR AI","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pfizer (Canada)","funders":"","keywords":"Preprint; Product (mathematics); Data science; Pharmaceutical industry; Computer science; Business; World Wide Web; 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":[],"consensus_categories":[],"category_scores_codex":[0.002038751,0.00009347401,0.0001201555,0.0001291231,0.000106081,0.0001307451,0.0001527912,0.0001685926,0.00001167342],"category_scores_gemma":[0.001580365,0.00007740294,0.0000653002,0.0002451715,0.0004786663,0.00002227471,0.0000760523,0.00008756805,0.000002654539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000187181,"about_ca_system_score_gemma":0.0003117308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004179795,"about_ca_topic_score_gemma":0.000001069508,"domain_scores_codex":[0.9988275,0.0001478311,0.0003332517,0.0002545736,0.0002736745,0.0001631879],"domain_scores_gemma":[0.9994679,0.00008531476,0.00007291916,0.0001899314,0.0001292281,0.00005467208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008461896,0.00009638926,0.00003011032,0.0004737349,0.00006038468,0.000001025133,0.001254145,0.0001367736,0.2105988,0.001373123,0.002944555,0.7821848],"study_design_scores_gemma":[0.0002290729,0.0004032512,0.0001670007,0.0003781166,0.00005419727,0.00003871521,0.001465251,0.1247195,0.7583006,0.002797696,0.1111199,0.0003266227],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1635749,0.0003168456,0.8347923,0.0003953699,0.0005395742,0.00027747,0.0000218652,0.00002757419,0.00005409708],"genre_scores_gemma":[0.934455,0.000005772692,0.06483225,0.00006922238,0.0002066005,0.00007164261,0.0002357514,0.000009568073,0.0001141857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7818581,"threshold_uncertainty_score":0.31564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08223819874697301,"score_gpt":0.4333640955664769,"score_spread":0.3511258968195039,"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."}}