{"id":"W3163573989","doi":"10.3389/fneur.2021.664631","title":"Recurrent Traumatic Brain Injury Surveillance Using Administrative Health Data: A Bayesian Latent Class Analysis","year":2021,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Incidence (geometry); Epidemiology; Gold standard (test); Psychological intervention; Radiological weapon; Latent class model; Pediatrics; Surgery; Internal medicine; Statistics; Psychiatry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.04346264,0.001105515,0.00201064,0.004268565,0.001157447,0.002984447,0.003444775,0.00148827,0.001630432],"category_scores_gemma":[0.08405184,0.001143853,0.003344132,0.003375382,0.001477451,0.002156129,0.002638573,0.002434565,0.0004326665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002911007,"about_ca_system_score_gemma":0.002890304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06275209,"about_ca_topic_score_gemma":0.02890739,"domain_scores_codex":[0.9762009,0.01745057,0.00117858,0.002876264,0.001541326,0.0007522941],"domain_scores_gemma":[0.9442765,0.03868523,0.009105785,0.004065735,0.003102969,0.0007638058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009368995,0.0005980583,0.7450722,0.0004661629,0.002713738,0.0003013201,0.001313737,0.1531122,0.0003110043,0.01636438,0.004603237,0.07420703],"study_design_scores_gemma":[0.0001623614,0.0002647556,0.09355796,0.0002928043,0.0007033027,0.0001788896,0.0003661463,0.8841189,0.0002110383,0.01761001,0.002432478,0.000101259],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.620136,0.003242201,0.3610633,0.003448174,0.0001657649,0.001396183,0.007450343,0.000447959,0.002650099],"genre_scores_gemma":[0.9388453,0.0007291132,0.05403547,0.0002207815,0.0001322212,0.0005141404,0.00496076,0.00003136524,0.0005309478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06275209,"threshold_uncertainty_score":0.2298551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1850602182742422,"score_gpt":0.4288899092135062,"score_spread":0.243829690939264,"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."}}