{"id":"W4400448303","doi":"10.1002/epi4.13008","title":"Epileptic seizure forecasting with wearable‐based nocturnal sleep features","year":2024,"lang":"en","type":"article","venue":"Epilepsia Open","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Institut TransMedTech; Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Epilepsy; Non-rapid eye movement sleep; Sleep (system call); Wakefulness; Heart rate; Nocturnal; Heart rate variability; Slow-wave sleep; Eye movement; Polysomnography; Medicine; Psychology; Electroencephalography; Computer science; Artificial intelligence; Internal medicine; Psychiatry; Blood pressure","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":[0.0002350516,0.0003866603,0.0002836694,0.0003447137,0.00008766478,0.0002398151,0.0001763336,0.0001672686,0.0004354],"category_scores_gemma":[0.0008557694,0.00008823491,0.0002326019,0.0002548817,0.0000435662,0.0001905773,0.0001694285,0.0001962567,0.0002085523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00013386,"about_ca_system_score_gemma":0.0002348221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002768212,"about_ca_topic_score_gemma":0.003318106,"domain_scores_codex":[0.999915,0.0000174354,0.00001024443,0.00002620616,0.00002049598,0.00001064697],"domain_scores_gemma":[0.9997981,0.00007199819,0.00004250932,0.00001545324,0.00005917396,0.00001284159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008557292,0.000419069,0.1999472,0.0001358698,0.000210712,0.0003043116,0.00009510449,0.1122157,0.02532058,0.0002105507,0.002264437,0.6580207],"study_design_scores_gemma":[0.00003711808,0.0002752735,0.0744432,0.00001881826,0.0000516913,0.0002380859,0.00004236088,0.9182357,0.005567686,0.0002500328,0.000825107,0.00001497535],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9127131,0.0005471093,0.08399876,0.0001706111,0.00005904911,0.00005622803,0.000528191,0.0008083277,0.001118715],"genre_scores_gemma":[0.9840411,0.0001141802,0.014909,0.00001788582,0.00002197841,0.00002025847,0.000415962,0.00000883816,0.0004508248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002768212,"threshold_uncertainty_score":0.005504191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04821499639053535,"score_gpt":0.2966470809432136,"score_spread":0.2484320845526782,"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."}}