{"id":"W4366291455","doi":"10.1101/2023.04.13.23288435","title":"Optimizing Detection and Deep Learning-based Classification of Pathological High-Frequency Oscillations in Epilepsy","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pathological; Detector; Epilepsy; Ictal; Epilepsy surgery; Artificial intelligence; Pattern recognition (psychology); Medicine; Pathology; Computer science; Neuroscience; Optics; Physics; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009581795,0.0006249669,0.000308411,0.0006679788,0.0001394223,0.0004803615,0.000366822,0.0004438969,0.0003991197],"category_scores_gemma":[0.002819128,0.0001331742,0.0003708761,0.0004110101,0.0002804954,0.0005988543,0.0004736444,0.0002810313,0.0001473337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004839369,"about_ca_system_score_gemma":0.0005729847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002307125,"about_ca_topic_score_gemma":0.003323314,"domain_scores_codex":[0.9996462,0.00009170774,0.00003415165,0.00009558139,0.00007984754,0.00005244536],"domain_scores_gemma":[0.999411,0.0002642611,0.0001279974,0.00003570387,0.0001301054,0.00003105724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005622817,0.0004404392,0.1565217,0.000368401,0.0003171393,0.0001778933,0.0001382036,0.1386657,0.07662322,0.001356996,0.001293311,0.6235347],"study_design_scores_gemma":[0.00002950049,0.0002341592,0.04878564,0.00003073108,0.00008268016,0.0001567429,0.00006276176,0.9250641,0.02367685,0.001221381,0.0006327026,0.00002280032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7367647,0.001289388,0.2595432,0.0003032855,0.0000275444,0.00008319689,0.0002509938,0.0004631066,0.001274581],"genre_scores_gemma":[0.9614127,0.0002327558,0.03742864,0.00006741403,0.00001818086,0.0000406152,0.0002094402,0.00001659357,0.0005736763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002307125,"threshold_uncertainty_score":0.005067348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05993519765525696,"score_gpt":0.3212142047961602,"score_spread":0.2612790071409032,"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."}}