{"id":"W2609716146","doi":"10.1093/sleepj/zsx050.068","title":"0069 DEVELOPMENT AND VALIDATION OF AN ALGORITHM FOR THE STUDY OF SLEEP USING A BIOMETRIC SHIRT IN YOUNG HEALTHY ADULTS","year":2017,"lang":"en","type":"article","venue":"SLEEP","topic":"Sleep and related disorders","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Hôpital Rivière-des-Prairies; Carré Technologies (Canada)","funders":"","keywords":"Polysomnography; Heart rate; Non-rapid eye movement sleep; Audiology; Medicine; Sleep (system call); Respiratory rate; Sleep medicine; Sleep onset; Algorithm; Eye movement; Psychology; Speech recognition; Apnea; Computer science; Anesthesia; Insomnia; Sleep disorder; Internal medicine; Psychiatry; Blood pressure; Ophthalmology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004719029,0.00009667511,0.0001786274,0.0003531907,0.0002080141,0.00001766761,0.0002073234,0.0000948362,0.00001426379],"category_scores_gemma":[0.00004949823,0.00007289084,0.0000251127,0.0002846078,0.00006329993,0.00008691302,0.0000453576,0.00008076211,0.000001421532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002558505,"about_ca_system_score_gemma":0.00001766461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002231922,"about_ca_topic_score_gemma":0.0004866596,"domain_scores_codex":[0.9990352,0.00006941648,0.0003453026,0.0002250231,0.0001415904,0.0001834087],"domain_scores_gemma":[0.9991678,0.00007535917,0.0002906564,0.0003602548,0.00006845678,0.00003746097],"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":[0.0005130784,0.001622958,0.07342283,0.00002832287,0.0002934561,0.000002222619,0.02362961,0.00007141329,0.00002988328,0.00003370157,0.000004948115,0.9003476],"study_design_scores_gemma":[0.0241327,0.003574484,0.8795547,0.00008592177,0.0003465714,0.00001233832,0.05018113,0.04055499,0.0008824315,0.0001197115,0.0001553218,0.0003996659],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954026,0.0005656125,0.002262339,0.00002704159,0.0005723779,0.0009416144,0.000005807247,0.000007746937,0.0002149056],"genre_scores_gemma":[0.9973962,0.00001622627,0.002474831,0.00001066809,0.00002359592,0.00004858426,0.000007163325,0.00001329802,0.000009466969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8999479,"threshold_uncertainty_score":0.3374013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04078028043863485,"score_gpt":0.3505906302410934,"score_spread":0.3098103498024585,"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."}}