{"id":"W2808345718","doi":"10.1186/s12911-018-0621-y","title":"Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review","year":2018,"lang":"en","type":"review","venue":"BMC Medical Informatics and Decision Making","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto; Public Health Ontario; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; Health Canada; Canada Research Chairs","keywords":"Social media; Health informatics; Pharmacovigilance; Data extraction; Computer science; Reliability (semiconductor); Data science; MEDLINE; Medicine; World Wide Web; Public health; Adverse effect; Nursing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06346302,0.001886184,0.00634654,0.05599152,0.001839332,0.008971779,0.003532672,0.005683899,0.003717354],"category_scores_gemma":[0.2739929,0.001804048,0.007734931,0.03710089,0.003707336,0.009413061,0.004838889,0.003065147,0.0007317512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007414765,"about_ca_system_score_gemma":0.02634102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007467794,"about_ca_topic_score_gemma":0.01163606,"domain_scores_codex":[0.9365918,0.02709096,0.02260734,0.002790791,0.01024738,0.0006717598],"domain_scores_gemma":[0.5466278,0.3970214,0.02587651,0.005442967,0.02410328,0.000927966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001367408,0.00005057267,0.001862629,0.7776774,0.002764633,0.0001772248,0.001393222,0.0003621538,0.0001995566,0.002803214,0.003932886,0.2086398],"study_design_scores_gemma":[0.00002433749,0.00005687049,0.001017162,0.970464,0.003336601,0.0001518075,0.0005128984,0.0001826365,0.0001405928,0.001194067,0.02288953,0.00002953268],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009247019,0.9928428,0.001248586,0.002140398,0.0003893027,0.0008547374,0.0004602022,0.0000190893,0.001120162],"genre_scores_gemma":[0.01326954,0.9769723,0.004709123,0.001469258,0.0003911821,0.002527547,0.0004853118,0.00001901806,0.0001567351],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.06346302,"threshold_uncertainty_score":0.3356285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5075659774165958,"score_gpt":0.6059076082814882,"score_spread":0.09834163086489234,"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."}}