{"id":"W3048354284","doi":"10.1111/epi.16633","title":"Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy","year":2020,"lang":"en","type":"article","venue":"Epilepsia","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; McGill University; Montreal Neurological Institute and Hospital","funders":"Medical Research Council; Alan Turing Institute","keywords":"Epilepsy; Big data; Data science; Computer science; Medicine; Psychiatry; Data mining","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.002187754,0.0002077176,0.0003171878,0.00004269111,0.0002179995,0.0001109603,0.002295613,0.0002063228,0.00003377496],"category_scores_gemma":[0.006295734,0.0001440266,0.0001028704,0.0002092654,0.000722769,0.00001980533,0.004341558,0.0005047401,0.00001179054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001202989,"about_ca_system_score_gemma":0.0006363133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003104906,"about_ca_topic_score_gemma":0.0006871384,"domain_scores_codex":[0.9965791,0.0005436162,0.0009442432,0.001165138,0.0004537267,0.0003142256],"domain_scores_gemma":[0.9956012,0.0005618459,0.0002892181,0.003166852,0.0002110566,0.0001698075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002502163,0.000218775,0.7522805,0.00002100621,0.0002632061,0.0001555777,0.0002078493,0.00004333096,0.02617544,0.0003297938,0.2083169,0.01173747],"study_design_scores_gemma":[0.002181385,0.0001776845,0.643983,0.0001121095,0.00009543146,0.00008616229,0.0008905911,0.003580415,0.003220071,0.002099494,0.3430849,0.0004887348],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728857,0.002031201,0.0003135711,0.01765816,0.001481952,0.0005936947,0.003939989,0.00001142467,0.001084305],"genre_scores_gemma":[0.9878859,0.001534569,0.0003583824,0.003013238,0.002206856,0.00001821946,0.004837336,0.00003240371,0.0001131143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.134768,"threshold_uncertainty_score":0.7537037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.183560606791995,"score_gpt":0.3708631319615985,"score_spread":0.1873025251696036,"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."}}