{"id":"W4306784188","doi":"10.1002/hbm.26118","title":"Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study","year":2022,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; National Institute of Neurological Disorders and Stroke; Medical Research Council; Horizon 2020 Framework Programme; Medical Research Council Canada; Wellcome Trust","keywords":"Magnetoencephalography; Hidden Markov model; Ictal; Epilepsy; Computer science; Psychology; Medicine; Neuroscience; Artificial intelligence; Electroencephalography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001221717,0.0004634912,0.0002886885,0.0003617985,0.0001255983,0.0003095669,0.0001855283,0.0003500317,0.0003660425],"category_scores_gemma":[0.00561546,0.0001638444,0.0004449182,0.0002372789,0.0002337736,0.000369784,0.0003223145,0.0003674727,0.0001247947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002519133,"about_ca_system_score_gemma":0.0002773141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004393853,"about_ca_topic_score_gemma":0.004710742,"domain_scores_codex":[0.9996719,0.0001750514,0.00002505709,0.0000714594,0.00003368412,0.00002285624],"domain_scores_gemma":[0.997107,0.00245059,0.0001524325,0.0001316131,0.0000905518,0.00006783252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001264726,0.0003544508,0.557608,0.0001822178,0.0008957277,0.0024033,0.0009852028,0.2944901,0.02413261,0.001548634,0.0007789093,0.1153562],"study_design_scores_gemma":[0.0000449109,0.0005638488,0.14278,0.00002321683,0.0001513586,0.001517084,0.000208956,0.8476245,0.005354163,0.00122618,0.0004592863,0.00004640875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9657123,0.0002334884,0.03352701,0.0001126969,0.000004517764,0.00001559291,0.0001256734,0.00006547122,0.000203248],"genre_scores_gemma":[0.991808,0.0001226325,0.007775122,0.00001852322,0.000006861719,0.000008910451,0.0001709335,0.0000158176,0.00007313062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004393853,"threshold_uncertainty_score":0.008736551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05984774196531093,"score_gpt":0.3310492919825218,"score_spread":0.2712015500172108,"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."}}