{"id":"W4398250977","doi":"10.1136/jnnp-2024-333532","title":"Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales","year":2024,"lang":"en","type":"article","venue":"Journal of Neurology Neurosurgery & Psychiatry","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Medical Research Council; Multiple Sclerosis Society","keywords":"Medicine; Epidemiology; Population; Record linkage; Cohort; Health care; Identification (biology); Retrospective cohort study; Data mining; Family medicine; Pediatrics; Computer science; Pathology; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"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":[0.009584349,0.0005166415,0.0008397315,0.005192757,0.0008193568,0.002593792,0.001675118,0.0009316959,0.001187399],"category_scores_gemma":[0.03948603,0.0005428787,0.0006552032,0.002793174,0.0007117969,0.001321615,0.002623178,0.0007530981,0.0003922789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001946489,"about_ca_system_score_gemma":0.004532243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01620058,"about_ca_topic_score_gemma":0.01796885,"domain_scores_codex":[0.9939368,0.003131482,0.0007782286,0.001153237,0.0007985595,0.000201634],"domain_scores_gemma":[0.9868184,0.008506783,0.001337831,0.0006727109,0.002369907,0.0002942989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005322907,0.0005095085,0.627945,0.000850577,0.0004836136,0.0009482974,0.002596622,0.06559098,0.002516271,0.008901381,0.005270203,0.2838552],"study_design_scores_gemma":[0.0003024755,0.0004384215,0.1031559,0.0004104086,0.0002434607,0.002127192,0.003350656,0.8444002,0.002864107,0.03091931,0.01168349,0.0001042499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5642458,0.0009509351,0.4172497,0.003627456,0.00008025302,0.004019394,0.002607986,0.001092689,0.006125685],"genre_scores_gemma":[0.5426427,0.0002934847,0.4519695,0.0004307976,0.00003752658,0.001246935,0.002496545,0.00004273772,0.00083984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01620058,"threshold_uncertainty_score":0.05068749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1418177783914742,"score_gpt":0.3499085718292916,"score_spread":0.2080907934378173,"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."}}