{"id":"W4405566965","doi":"10.2196/51615","title":"Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science and Technology Council; Shanghai Educational Development Foundation; Case Western Reserve University; University of Washington; York University; Johns Hopkins University; National Heart, Lung, and Blood Institute; University of California, Davis; University of Minnesota","keywords":"Obstructive sleep apnea; Pulse oximetry; Pittsburgh Sleep Quality Index; Polysomnography; Medicine; Sleep apnea; Oxygen saturation; Test set; Physical therapy; Receiver operating characteristic; Gold standard (test); Artificial intelligence; Machine learning; Apnea; Computer science; Internal medicine; Sleep quality; Insomnia; Anesthesia","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":[0.01117758,0.001746459,0.00128363,0.001119549,0.000409894,0.001176061,0.001678644,0.001060848,0.001423443],"category_scores_gemma":[0.01684635,0.0006799541,0.003486997,0.0006815465,0.0004134396,0.001093026,0.001097722,0.0009741274,0.0004085433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007807558,"about_ca_system_score_gemma":0.0009000496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006117615,"about_ca_topic_score_gemma":0.003466544,"domain_scores_codex":[0.9955124,0.00308563,0.0002291796,0.0007556637,0.0002724995,0.0001445929],"domain_scores_gemma":[0.9826936,0.01404696,0.0006825948,0.001342135,0.001026461,0.0002083499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.007357129,0.00389774,0.2336883,0.001357871,0.007592465,0.0004804895,0.0005084617,0.3187233,0.004824725,0.00380494,0.002628008,0.4151367],"study_design_scores_gemma":[0.00009256269,0.0008031963,0.01429769,0.0000363418,0.0004800898,0.00009012637,0.00007683346,0.9812822,0.001320097,0.0009081098,0.0005698705,0.00004291051],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6513656,0.00426369,0.3401304,0.0004248194,0.0001696922,0.000774483,0.001393651,0.0005889478,0.0008887895],"genre_scores_gemma":[0.9071641,0.0005371287,0.08728946,0.0001658393,0.0001307739,0.000806734,0.002594053,0.00007323758,0.001238635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01117758,"threshold_uncertainty_score":0.05911344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1226558640217253,"score_gpt":0.4727343344525338,"score_spread":0.3500784704308085,"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."}}