{"id":"W2927462243","doi":"10.2196/11264","title":"Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine","keywords":"Pharmacovigilance; Medicine; Population; Robustness (evolution); Public health; Clinical trial; Adverse effect; Internal medicine; Nursing; Environmental health","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":[0.001642432,0.0001833563,0.0004042221,0.0002419142,0.0004478218,0.00001489769,0.00009457942,0.000149881,0.00002794861],"category_scores_gemma":[0.00005155235,0.0001883454,0.0000876565,0.0005429064,0.00007459725,0.0003474039,0.00002392643,0.0007503512,0.000005630688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002267258,"about_ca_system_score_gemma":0.0004549323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002620921,"about_ca_topic_score_gemma":0.0006873888,"domain_scores_codex":[0.9978201,0.0004669381,0.0005790522,0.0003392148,0.0001315364,0.0006631963],"domain_scores_gemma":[0.9987842,0.0002583054,0.0004463608,0.00009143204,0.0001212828,0.0002984356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00154829,0.0006660247,0.08038435,0.001739812,0.00007232199,0.000003734044,0.008749031,0.0008615445,0.01796493,0.00008631088,0.001260745,0.8866629],"study_design_scores_gemma":[0.01250322,0.0005852077,0.1701327,0.0001040013,0.000009005878,0.000056662,0.008031387,0.4978589,0.0008907872,0.00005652333,0.3088644,0.0009071752],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893813,0.002390854,0.002159848,0.003178114,0.0008911625,0.00156751,0.00007807705,0.0001196344,0.0002335196],"genre_scores_gemma":[0.9967545,0.0002904969,0.00002577005,0.001830679,0.0004150653,0.00018904,0.00006878696,0.00002121504,0.0004044025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8857557,"threshold_uncertainty_score":0.7680503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05015372774896675,"score_gpt":0.415397698240466,"score_spread":0.3652439704914993,"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."}}