{"id":"W4399191423","doi":"10.2196/59167","title":"The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Pharmacovigilance; Social media; Value (mathematics); Data science; Internet privacy; Medicine; Computer science; Adverse effect; World Wide Web; Pharmacology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002667156,0.000176138,0.0004574053,0.0001536238,0.000723297,0.00002110289,0.0001243351,0.0001068531,0.00004482765],"category_scores_gemma":[0.0002425375,0.0001384504,0.0001649913,0.0007817919,0.0001625734,0.0001965371,0.00004237151,0.0003896999,0.000004209068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006500586,"about_ca_system_score_gemma":0.0004511045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006368731,"about_ca_topic_score_gemma":0.00007834929,"domain_scores_codex":[0.9979566,0.0005098147,0.0005252188,0.0003402458,0.0001636363,0.0005044407],"domain_scores_gemma":[0.9976292,0.001604289,0.0002059155,0.0001059341,0.0001063335,0.0003483797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009576782,0.0004694688,0.105865,0.0613271,0.004634598,0.00001794009,0.006791352,0.0001479794,0.003664735,0.007139194,0.02094902,0.788036],"study_design_scores_gemma":[0.002046296,0.0001094816,0.02013807,0.0009333327,0.0001881824,0.0000263215,0.0004925364,0.01916958,0.0001609585,0.0002054647,0.9560512,0.0004786173],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5096502,0.4047501,0.00394131,0.06354436,0.006326403,0.008226521,0.001404453,0.0006045894,0.001552067],"genre_scores_gemma":[0.8968973,0.0992162,0.00001923999,0.002988373,0.0002975452,0.0004105456,0.00006188889,0.00001534277,0.00009353145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9351022,"threshold_uncertainty_score":0.5645842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1209939208437684,"score_gpt":0.4878111121992146,"score_spread":0.3668171913554462,"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."}}