{"id":"W3023864499","doi":"10.2196/17252","title":"Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; FedDev Ontario; Toronto Rehabilitation Institute","keywords":"Apnea; Medicine; Obstructive sleep apnea; Central sleep apnea; Sleep apnea; Breathing; Hypopnea; Anesthesia; Polysomnography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005513395,0.0001783674,0.000709122,0.0007085668,0.0000575464,0.00008030774,0.0007688332,0.0002221899,0.0009796565],"category_scores_gemma":[0.05568884,0.0001488905,0.0001750524,0.001213363,0.0004492674,0.0002762316,0.0005745416,0.00428223,0.000009373861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008173213,"about_ca_system_score_gemma":0.0004031308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003815963,"about_ca_topic_score_gemma":0.00003543336,"domain_scores_codex":[0.9899601,0.001562266,0.001228542,0.0003505375,0.006191936,0.0007065961],"domain_scores_gemma":[0.994956,0.001560146,0.0004085563,0.000202513,0.001825277,0.001047501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01625127,0.001797357,0.8792492,0.0004582686,0.00108467,0.005292184,0.02801663,0.0006831699,0.007302867,0.0002742823,0.0001408294,0.05944928],"study_design_scores_gemma":[0.03820725,0.01915568,0.4688941,0.001639773,0.0003040274,0.000558874,0.0645818,0.3860767,0.01885656,0.0003333276,0.0009025783,0.0004892958],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950643,0.0002142986,0.001690265,0.001487912,0.000367242,0.0004942072,0.000001285749,0.00001045373,0.0006700845],"genre_scores_gemma":[0.9984702,0.00002155292,0.0007668617,0.0000221035,0.0006513644,0.0000040273,0.000004041709,0.00003399271,0.00002582389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4103551,"threshold_uncertainty_score":0.9999336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1236979119403198,"score_gpt":0.4341055040351203,"score_spread":0.3104075920948005,"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."}}