{"id":"W2042565541","doi":"10.1016/j.clinph.2014.06.003","title":"Clinical Neurophysiology in multiple sclerosis – From diagnostic tool to biomarker","year":2014,"lang":"en","type":"letter","venue":"Clinical Neurophysiology","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Parkinson Schweiz; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Mach-Gaensslen Foundation of Canada; Freiwillige Akademische Gesellschaft","keywords":"Multiple sclerosis; Biomarker; Neurophysiology; Clinical neurophysiology; Medicine; Neuroscience; Physical medicine and rehabilitation; Psychology; Biology; Electroencephalography; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005985392,0.0006856047,0.00198624,0.001389992,0.002281546,0.00320045,0.001579208,0.02870834,0.002242217],"category_scores_gemma":[0.06246204,0.0006330253,0.0008664965,0.001224806,0.004348098,0.003590537,0.001494993,0.01696187,0.002227763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003593842,"about_ca_system_score_gemma":0.003697886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002957003,"about_ca_topic_score_gemma":0.005224981,"domain_scores_codex":[0.9930987,0.003071993,0.001703735,0.0004307381,0.0012289,0.0004660722],"domain_scores_gemma":[0.9705808,0.02078465,0.001843217,0.0008241819,0.004039489,0.001927634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004058158,0.000155074,0.01091664,0.0005034196,0.0001232594,0.02652119,0.0004095295,0.0002174105,0.0008831886,0.007454623,0.8955144,0.05689558],"study_design_scores_gemma":[0.001770343,0.0006601847,0.02191537,0.005158501,0.0005405874,0.1353071,0.002432946,0.004692454,0.002109251,0.1021791,0.722885,0.000349123],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001434309,0.004995423,0.0002255872,0.9778267,0.01280091,0.00001143508,0.00002935884,0.0000169455,0.002659391],"genre_scores_gemma":[0.02983854,0.005652745,0.001060565,0.812112,0.1477602,0.00008006932,0.00004162603,0.00001911556,0.003435241],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02870834,"threshold_uncertainty_score":0.03165418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2375705685794368,"score_gpt":0.4045510909197459,"score_spread":0.1669805223403091,"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."}}