{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003582599,0.00007858623,0.000307357,0.0001320992,0.00003203236,0.000004939317,0.00007320972,0.00005180225,0.0000465016],"category_scores_gemma":[0.0002335692,0.00007108272,0.00003003952,0.0001040364,0.00006628107,0.00008968303,0.00004865827,0.0001516402,0.000009399361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001981378,"about_ca_system_score_gemma":0.00011416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004237862,"about_ca_topic_score_gemma":0.0003565183,"domain_scores_codex":[0.9988986,0.0001700679,0.0002633997,0.0002674047,0.0001573148,0.0002432719],"domain_scores_gemma":[0.9991192,0.0004039855,0.0001020922,0.0002385535,0.00006238576,0.00007383409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.009682281,0.003900836,0.2445708,0.003099791,0.0003873599,0.0005574799,0.005732144,0.0001603488,0.1564957,0.0005070395,0.01073414,0.5641721],"study_design_scores_gemma":[0.009438515,0.02727436,0.90254,0.000108989,0.00003236932,0.000210706,0.0003831639,0.01766427,0.03681201,0.0002135966,0.005093475,0.0002285289],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965456,0.00005130394,0.0002637344,0.001487977,0.0001254606,0.0009980447,0.0004720921,0.00001291407,0.00004284196],"genre_scores_gemma":[0.9963747,0.00007289148,0.002093686,0.0005942465,0.00005626537,0.0000572363,0.0007269668,0.0000111815,0.0000128709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6579692,"threshold_uncertainty_score":0.2898669,"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."}}