{"id":"W2507679269","doi":"10.1002/pds.4090","title":"Can social media data lead to earlier detection of drug‐related adverse events?","year":2016,"lang":"en","type":"article","venue":"Pharmacoepidemiology and Drug Safety","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis","funders":"","keywords":"Medicine; Adverse Event Reporting System; Social media; Sibutramine; Atorvastatin; Adverse effect; Internal medicine; World Wide Web; Weight loss; Computer science; Obesity","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004148549,0.000419637,0.0007662373,0.0002611436,0.0005487739,0.000002029444,0.0006622073,0.0003559257,0.001092354],"category_scores_gemma":[0.001192918,0.0003322387,0.0001721568,0.0003980695,0.0005371706,0.0004124657,0.0004787339,0.0009199004,0.0003120566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001196482,"about_ca_system_score_gemma":0.0001258329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009303048,"about_ca_topic_score_gemma":0.0001895216,"domain_scores_codex":[0.994893,0.002175582,0.001055426,0.0008690659,0.0001820396,0.0008249087],"domain_scores_gemma":[0.9937964,0.004705303,0.0004471419,0.0004271389,0.0001314053,0.0004925781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004473758,0.0007226681,0.06267213,0.000154698,0.00148942,0.00006015003,0.005457672,0.0003294546,0.6191444,0.002042016,0.05665132,0.2468023],"study_design_scores_gemma":[0.01537673,0.00008755146,0.06672145,0.0001283397,0.001697075,0.0001373763,0.001349657,0.002797223,0.1436328,0.004072513,0.7623568,0.001642443],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.953292,0.0007917929,0.001042638,0.0342339,0.004474957,0.0008322616,0.002668122,0.0002744918,0.002389799],"genre_scores_gemma":[0.9917066,0.00242158,0.0001135084,0.003816941,0.0006036005,0.00005318413,0.0001476059,0.00003960287,0.00109736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7057055,"threshold_uncertainty_score":0.999913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1251523078739059,"score_gpt":0.434621748955007,"score_spread":0.3094694410811011,"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."}}