{"id":"W2333066168","doi":"10.2196/resprot.5028","title":"Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study","year":2016,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; Fogarty International Center; National Institute on Aging; National Institutes of Health","keywords":"Drug; Product (mathematics); Pharmacokinetics; Computer science; Drug labeling; Pharmacology; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04678256,0.001491997,0.0009082548,0.001361757,0.002462249,0.002282363,0.002880674,0.002846452,0.007055198],"category_scores_gemma":[0.08011389,0.001365771,0.001114181,0.0004945617,0.001757056,0.004633451,0.005832629,0.002012316,0.003610719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097963,"about_ca_system_score_gemma":0.00287483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002730142,"about_ca_topic_score_gemma":0.005187161,"domain_scores_codex":[0.9748199,0.01492246,0.001565262,0.004456028,0.002653875,0.00158241],"domain_scores_gemma":[0.8861618,0.06850404,0.004709494,0.0115381,0.02199075,0.00709584],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.02308722,0.1164337,0.2213319,0.005300724,0.0003933512,0.006294253,0.218664,0.00350847,0.08714854,0.001452178,0.008784408,0.3076013],"study_design_scores_gemma":[0.01897408,0.1898377,0.4156028,0.001564828,0.001196031,0.00911386,0.1475199,0.05411783,0.0716507,0.006990456,0.08182209,0.001609686],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"protocol","genre_scores_codex":[0.9760088,0.0000648905,0.01389494,0.00030577,0.00006412374,0.006943177,0.0002381477,0.0002755713,0.002204586],"genre_scores_gemma":[0.8858073,0.0001540821,0.08966766,0.00111336,0.0001229547,0.01916777,0.0007742643,0.0001410616,0.003051682],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.9532174,"threshold_uncertainty_score":0.2474128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5018194849802321,"score_gpt":0.6310378209124399,"score_spread":0.1292183359322078,"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."}}