{"id":"W4410620035","doi":"10.1016/j.engappai.2025.111154","title":"Convolutional self-attention with adaptive channel-attention network for obstructive sleep apnea detection using limited training data","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Computer science; Obstructive sleep apnea; Channel (broadcasting); Training (meteorology); Sleep (system call); Artificial intelligence; Computer network; Medicine; Cardiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003808099,0.0001871236,0.000273861,0.000357906,0.000218459,0.00002645398,0.0002522041,0.0001141602,0.000005515674],"category_scores_gemma":[0.000147892,0.0001992406,0.00007507461,0.00125429,0.0001241712,0.0002048924,0.0000903292,0.0002449374,0.000004121883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002205238,"about_ca_system_score_gemma":0.00007282527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003291249,"about_ca_topic_score_gemma":0.00001531752,"domain_scores_codex":[0.9983859,0.00002528174,0.0004657558,0.0005139852,0.0002719477,0.0003371331],"domain_scores_gemma":[0.9982888,0.0002464182,0.0001608033,0.0005481222,0.0006764524,0.00007939268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001454692,0.0006340867,0.0009831763,0.0006788673,0.001658091,0.000002676142,0.000560481,0.4215745,0.1602135,0.06993353,0.000007832295,0.3422986],"study_design_scores_gemma":[0.0001494453,0.0001544036,0.003245659,0.0001055855,0.00024743,0.00001312649,0.0009044497,0.9773363,0.01608873,0.001512971,0.00008389136,0.0001580037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07129015,0.0001007789,0.9265478,0.00008527716,0.0001682868,0.001564448,0.00007273232,0.0001330561,0.00003748154],"genre_scores_gemma":[0.8786735,0.000003296298,0.120501,0.000006864533,0.0002389052,0.0003683598,0.0001718662,0.00002713116,0.000009083877],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8073833,"threshold_uncertainty_score":0.8124797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06122924971565081,"score_gpt":0.3176770022070134,"score_spread":0.2564477524913626,"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."}}