{"id":"W4409796480","doi":"10.1109/tbme.2025.3562067","title":"Deep Learning-Augmented Sleep Spindle Detection for Acute Disorders of Consciousness: Integrating CNN and Decision Tree Validation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Sleep and Wakefulness Research","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Decision tree; Deep learning; Persistent vegetative state; Computer science; Sleep (system call); Artificial intelligence; Minimally conscious state; Consciousness; Machine learning; Psychology; Neuroscience","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":[0.00223033,0.001084117,0.0006928721,0.0008584412,0.0002929742,0.0005916739,0.0009328351,0.0008662158,0.0007228705],"category_scores_gemma":[0.005413438,0.0003089177,0.0006750707,0.000501756,0.0002430629,0.0005812152,0.0007751712,0.001072111,0.0003499236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007217241,"about_ca_system_score_gemma":0.001043232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01166075,"about_ca_topic_score_gemma":0.01056152,"domain_scores_codex":[0.9995207,0.0001645173,0.00004690192,0.0001076828,0.00008389511,0.00007634061],"domain_scores_gemma":[0.9981546,0.001082959,0.0001281132,0.0001125166,0.0004426558,0.00007912423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006578977,0.0003905055,0.02041753,0.0001897107,0.000274078,0.0002937737,0.0001199748,0.489,0.009600866,0.001073914,0.006264277,0.4717175],"study_design_scores_gemma":[0.00001124216,0.00003839884,0.0009536177,0.00001213045,0.00001323925,0.00001660405,0.000007721496,0.9973221,0.0009518233,0.0004511822,0.0002182188,0.000003738365],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3915638,0.003630013,0.5951771,0.001160843,0.0003039888,0.0002359497,0.0009375489,0.004124025,0.002866646],"genre_scores_gemma":[0.9068952,0.0004525752,0.08807913,0.0003802181,0.00008484608,0.00011857,0.002188596,0.00008350701,0.001717409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01166075,"threshold_uncertainty_score":0.02318579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020868761729113,"score_gpt":0.2801106304590792,"score_spread":0.2699019428417881,"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."}}