{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003405876,0.000295794,0.0002911889,0.0001452784,0.0003082743,0.001768145,0.001186352,0.00009099497,0.0005218413],"category_scores_gemma":[0.0001429771,0.0002038142,0.00008907014,0.0005365601,0.0001277995,0.0006966575,0.0003179125,0.0005838795,0.0002553429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005452817,"about_ca_system_score_gemma":0.0001424255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005333923,"about_ca_topic_score_gemma":0.00004040882,"domain_scores_codex":[0.9978787,0.0001657744,0.0002653644,0.0007906095,0.000383734,0.0005158673],"domain_scores_gemma":[0.9987158,0.0006249522,0.00008010834,0.0003845583,0.0000359716,0.000158585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002276424,0.0008320723,0.005764346,0.001425426,0.0004156776,0.01256689,0.009173263,0.04836672,0.2340387,0.01825703,0.2542826,0.4126008],"study_design_scores_gemma":[0.002699163,0.002054774,0.004673128,0.004748903,0.0001443847,0.002889177,0.0003180293,0.3733763,0.4594292,0.003237552,0.1445557,0.001873709],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8266006,0.001402374,0.01162724,0.01151305,0.002986336,0.002241404,0.00009453527,0.0009620143,0.1425725],"genre_scores_gemma":[0.9874955,0.000008844326,0.004358812,0.002790013,0.0003028664,0.00005212307,0.000003686684,0.0000579379,0.004930255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4107271,"threshold_uncertainty_score":0.9992681,"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."}}