{"id":"W3030988535","doi":"10.1002/hbm.25037","title":"The <scp>ENIGMA‐Epilepsy</scp> working group: Mapping disease from large data sets","year":2020,"lang":"en","type":"review","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; European Regional Development Fund; Fonds de Recherche du Québec - Santé; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Stavros Niarchos Foundation; Hospital for Sick Children; National Natural Science Foundation of China; Eisai; Eberhard Karls Universität Tübingen; Medical Research Council; Medical Research Council Canada; National Health and Medical Research Council; Fundação de Amparo à Pesquisa do Estado de São Paulo; University of Melbourne; H. Lundbeck A/S; Monash University; Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; Deutsche Forschungsgemeinschaft; South African Medical Research Council; National Institute on Aging; Health Research Board; National Institute for Health and Care Research; Department of Health and Aged Care, Australian Government; European Commission; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Epilepsy Research UK; National Science Foundation; Science Foundation Ireland; Natural Sciences and Engineering Research Council of Canada; Biogen; Ministry of Health","keywords":"Epilepsy; Diffusion MRI; Neuroscience; Disease; Psychology; Clinical phenotype; Resting state fMRI; Data science; Computer science; Cognitive science; Medicine; Magnetic resonance imaging; Phenotype; Biology; Pathology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002066972,0.001191486,0.002033415,0.0003301585,0.00496948,0.001047554,0.004211737,0.0002922084,0.00004436873],"category_scores_gemma":[0.04645415,0.0009831086,0.000727463,0.001357712,0.0004281801,0.0004936481,0.005366638,0.001858248,0.0005649301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003226681,"about_ca_system_score_gemma":0.0002682397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001702177,"about_ca_topic_score_gemma":0.00004510611,"domain_scores_codex":[0.9907595,0.001999752,0.001298641,0.003335282,0.001204867,0.001401948],"domain_scores_gemma":[0.9413314,0.0540301,0.001074357,0.003077105,0.00004884484,0.0004381927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009739662,0.0002815703,0.0004042533,0.009739975,0.001059697,0.001265346,0.002720494,0.000004064471,0.000403347,0.02301819,0.4663304,0.4947629],"study_design_scores_gemma":[0.0002897791,0.00001714003,0.000604315,0.008427606,0.0002225241,0.00001333537,0.0004161378,0.0002447254,3.332423e-7,0.003158184,0.9862071,0.0003988007],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005008392,0.987558,0.001123052,0.00348029,0.001810518,0.00186095,0.00145698,0.0006718931,0.001988248],"genre_scores_gemma":[0.0005513513,0.9831797,0.0002820721,0.00930324,0.003344759,0.0004527143,0.001477362,0.0003330633,0.001075744],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5198767,"threshold_uncertainty_score":0.9999894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2150294874315168,"score_gpt":0.3422461086132984,"score_spread":0.1272166211817816,"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."}}