{"id":"W2118948764","doi":"10.1177/1352458514538334","title":"Identifying individuals with multiple sclerosis in an electronic medical record","year":2014,"lang":"en","type":"article","venue":"Multiple Sclerosis Journal","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University of Toronto; University Health Network; St. Michael's Hospital; Institute for Clinical Evaluative Sciences; Women's College Hospital","funders":"Canadian Institutes of Health Research","keywords":"Medical record; Medicine; Medical prescription; Electronic medical record; Diagnosis code; MEDLINE; Family medicine; Chart; Medical emergency; Internal medicine; Population; Nursing","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":[0.003683832,0.0001864751,0.0003118094,0.002583608,0.0003660673,0.0008914507,0.0005518563,0.000296905,0.001408392],"category_scores_gemma":[0.02032501,0.0001366833,0.0002298909,0.003344951,0.0002377119,0.0005680221,0.0006886681,0.0002222949,0.0003398844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001113888,"about_ca_system_score_gemma":0.001804267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06130677,"about_ca_topic_score_gemma":0.08088093,"domain_scores_codex":[0.9962335,0.001130214,0.0008282345,0.0003692814,0.001231967,0.0002066923],"domain_scores_gemma":[0.982515,0.005283409,0.008105079,0.0009502851,0.002775994,0.0003702347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006042334,0.00002783767,0.9873078,0.00009389391,0.00002829447,0.00008021536,0.0002728848,0.0001013298,0.0003320508,0.00005244882,0.0005906302,0.0110521],"study_design_scores_gemma":[0.00001899191,0.0000867413,0.9948726,0.0001108046,0.0000421339,0.0002398394,0.0003547724,0.001189845,0.0005655375,0.00007629246,0.002436134,0.000006339007],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844881,0.000642608,0.002355531,0.0005732369,0.00002073806,0.0005343112,0.00742426,0.00007464567,0.00388658],"genre_scores_gemma":[0.9875048,0.0004413145,0.006995631,0.000240559,0.00003757077,0.0001675341,0.004020574,0.000004595207,0.0005874617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06130677,"threshold_uncertainty_score":0.1218998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1088166259087771,"score_gpt":0.3238112316837923,"score_spread":0.2149946057750153,"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."}}