{"id":"W4385584115","doi":"10.1038/s41598-023-39799-8","title":"Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute; Université de Montréal; Institut Universitaire de Gériatrie de Montréal; Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"UCB Pharma; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Eisai Canada; Institut de Valorisation des Données; Eisai; Canada Research Chairs","keywords":"Electroencephalography; Ictal; Epilepsy; Receiver operating characteristic; Cohort; Medicine; Audiology; Set (abstract data type); Computer science; Artificial intelligence; Machine learning; Internal medicine; Psychiatry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002779652,0.0008255843,0.0008578745,0.001206779,0.0002276162,0.000646691,0.0005368676,0.0005336804,0.0009387436],"category_scores_gemma":[0.009237668,0.0002198477,0.0006460829,0.0005519849,0.000244917,0.0004462943,0.0004816036,0.0008763261,0.0004551056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004118593,"about_ca_system_score_gemma":0.0007145413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004688827,"about_ca_topic_score_gemma":0.003242175,"domain_scores_codex":[0.9992466,0.0003551082,0.00006804462,0.0001813892,0.00008123901,0.00006768684],"domain_scores_gemma":[0.9951996,0.003907074,0.0003278899,0.0001860027,0.0002915768,0.00008776441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001232003,0.0007008899,0.2397898,0.0001257609,0.0006183686,0.000393422,0.0001197832,0.4638035,0.004264404,0.0006939315,0.002325467,0.2859326],"study_design_scores_gemma":[0.0000171808,0.0001385712,0.01816099,0.00001089116,0.0000268308,0.00007090843,0.00001327946,0.9797326,0.0008334073,0.0008262832,0.0001561711,0.00001289608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.775411,0.001417779,0.2190726,0.0005000023,0.00006074156,0.0001628621,0.001004366,0.00135258,0.001018011],"genre_scores_gemma":[0.9739414,0.0002288375,0.02435932,0.00004864607,0.00004487148,0.0001084144,0.0008562783,0.00003230529,0.0003800536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004688827,"threshold_uncertainty_score":0.01470035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03171483699684336,"score_gpt":0.2670995915160652,"score_spread":0.2353847545192219,"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."}}