{"id":"W2911503056","doi":"10.1212/wnl.0000000000007043","title":"Validation of an algorithm for identifying MS cases in administrative health claims datasets","year":2019,"lang":"en","type":"article","venue":"Neurology","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre","funders":"Ministry of Health, Saskatchewan; National Multiple Sclerosis Society; U.S. Department of Veterans Affairs","keywords":"Inter-rater reliability; Medicine; Algorithm; Cohort; Predictive value; Positive predicative value; Youden's J statistic; Population; Reliability (semiconductor); Retrospective cohort study; Statistics; Demography; Computer science; Internal medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.04727979,0.001106897,0.001255827,0.004329951,0.001259298,0.003367829,0.002120069,0.001896296,0.001071733],"category_scores_gemma":[0.1304918,0.0004848472,0.001706793,0.00215394,0.0008201274,0.001780585,0.002088756,0.001415606,0.0006923513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002243758,"about_ca_system_score_gemma":0.005732815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009143341,"about_ca_topic_score_gemma":0.00629859,"domain_scores_codex":[0.9743134,0.01283077,0.004033775,0.004222019,0.003981114,0.0006189094],"domain_scores_gemma":[0.9167943,0.05194012,0.006304538,0.005068936,0.01923046,0.0006617816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001734509,0.0009097049,0.5796406,0.0006216138,0.001942703,0.0003712985,0.0007039238,0.1154033,0.004433681,0.003938344,0.01034624,0.279954],"study_design_scores_gemma":[0.0005256693,0.0003883457,0.07498324,0.0003483313,0.000325789,0.0005849052,0.0003353745,0.9053413,0.006522654,0.005850594,0.004723526,0.00007025003],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3259867,0.0007579676,0.6574646,0.001333733,0.0001814838,0.002595203,0.005250457,0.003596237,0.002833585],"genre_scores_gemma":[0.4173489,0.0001529213,0.5751374,0.0004073179,0.00005316103,0.001150285,0.005217112,0.0001447507,0.0003880328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04727979,"threshold_uncertainty_score":0.2500424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1829015924076665,"score_gpt":0.4578153429799989,"score_spread":0.2749137505723325,"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."}}