{"id":"W2947293212","doi":"10.14288/1.0374224","title":"Bayesian adjustments for disease misclassification in epidemiological studies of health administrative databases, with applications to multiple sclerosis research","year":2018,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Epidemiology; Multiple sclerosis; Disease; Bayesian probability; Database; Medicine; Data science; Computer science; Data mining; Artificial intelligence; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2058059,0.002228821,0.003124173,0.003664453,0.002190573,0.00450537,0.007947673,0.005222479,0.002169964],"category_scores_gemma":[0.5238568,0.00229111,0.004110573,0.004961751,0.004669886,0.006093408,0.006137004,0.006827886,0.0005399452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003148026,"about_ca_system_score_gemma":0.003956602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01269322,"about_ca_topic_score_gemma":0.01052431,"domain_scores_codex":[0.8436282,0.1318816,0.005221269,0.01019099,0.007859962,0.001218067],"domain_scores_gemma":[0.5472948,0.3786619,0.03010609,0.03133583,0.01129655,0.001304814],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000909213,0.0001976312,0.0675015,0.002096607,0.004748767,0.0006418342,0.003611349,0.1635841,0.001080044,0.3857148,0.009225931,0.3606883],"study_design_scores_gemma":[0.0002809113,0.0003552315,0.01857603,0.001157581,0.0009829336,0.0005667947,0.0003663126,0.3424702,0.001145257,0.6190709,0.01482437,0.0002034501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01122933,0.003339487,0.9803296,0.002958998,0.0003804262,0.0003180335,0.000240708,0.0002859618,0.0009174362],"genre_scores_gemma":[0.3225114,0.003938197,0.6641068,0.002417787,0.0008584066,0.001841248,0.0007931233,0.0002589315,0.003274137],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7941941,"threshold_uncertainty_score":0.9793827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2712509192296823,"score_gpt":0.3879644871705931,"score_spread":0.1167135679409108,"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."}}