{"id":"W4210460336","doi":"10.2196/preprints.18297","title":"A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Polysomnography; Obstructive sleep apnea; Vital signs; Apnea; Computer science; Sleep (system call); Sleep apnea; Medicine; Mean squared error; Remote patient monitoring; Real-time computing; Simulation; Medical emergency; Artificial intelligence; Statistics; Mathematics; Anesthesia","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001001393,0.0003497857,0.0004051225,0.0005799452,0.0001980744,0.0003805672,0.0003727368,0.0005601718,0.003467778],"category_scores_gemma":[0.001228679,0.0001111844,0.0005708177,0.0003068635,0.0002089104,0.000539529,0.0002730573,0.0002428908,0.0004839318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001858761,"about_ca_system_score_gemma":0.000197584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001711931,"about_ca_topic_score_gemma":0.001225887,"domain_scores_codex":[0.9995853,0.0001253081,0.00003785006,0.0001069243,0.0001153614,0.00002922339],"domain_scores_gemma":[0.9992144,0.0002179508,0.00005338614,0.00005810539,0.000410939,0.00004514829],"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":[0.03001109,0.008466202,0.2021157,0.003819813,0.001796957,0.001037998,0.00171854,0.002140541,0.3531137,0.0009021982,0.00496746,0.3899097],"study_design_scores_gemma":[0.001196586,0.1111406,0.7251945,0.0001624979,0.002947869,0.003570457,0.004204871,0.02487055,0.1163889,0.0004309308,0.00965167,0.0002405482],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9859939,0.001827631,0.008613172,0.00008088478,0.0003002379,0.00035314,0.0005349019,0.00006388502,0.002232287],"genre_scores_gemma":[0.9888247,0.000962712,0.007458837,0.0001116957,0.000103376,0.0002511861,0.0004744909,0.00001552695,0.001797449],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003467778,"threshold_uncertainty_score":0.01160085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07620532032116535,"score_gpt":0.30197681924653,"score_spread":0.2257714989253646,"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."}}