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Record W2943882008 · doi:10.1002/lary.28057

Upper airway stimulation therapy and sleep architecture in patients with obstructive sleep apnea

2019· article· en· W2943882008 on OpenAlexaff
Dominique Bohorquez, Ahmad F. Mahmoud, Jason L. Yu, Erica R. Thaler

Bibliographic record

VenueThe Laryngoscope · 2019
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsCanadian Sleep & Circadian Network
Fundersnot available
KeywordsMedicineObstructive sleep apneaSleep architectureSleep (system call)AnesthesiaBody mass indexAirwayApneaRetrospective cohort studySleep apneaPolysomnographySurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES/HYPOTHESIS: To quantify changes in sleep architecture before and after upper airway stimulation (UAS) therapy in patients with obstructive sleep apnea. STUDY DESIGN: Retrospective chart review. METHODS: This study was performed at a single-institution tertiary academic care center. Patients who responded successfully to UAS implantation were selected for this study. Preoperative and postoperative sleep studies were compared to determine sleep architecture changes. Primary outcomes included sleep architecture parameters such as N1, N2, N3, and rapid eye movement (REM) in addition to others. Secondary outcomes included body mass index. RESULTS: Thirty-five patients met inclusion criteria for this study. There was significant improvement across several sleep architecture parameters. N1 sleep percent decreased from 16.7% ± 2.1% preoperatively to 10.1% ± 1.6% postoperatively (P = .023). Time spent in N2 increased from 148.0 ± 12.4 minutes to 185.5 ± 10.4 minutes (P = .030), whereas N3 increased from 21.9 ± 5.0 minutes to 57.0 ± 11.1 minutes (P = .013). No significant changes were observed in REM sleep. Arousal index decreased from 38.8 ± 4.0 to 30.3 ± 4.0 (P = .050). CONCLUSIONS: There was significant improvement across several sleep architecture parameters among patients who responded successfully to UAS implantation. LEVEL OF EVIDENCE: 4 Laryngoscope, 130:1085-1089, 2020.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.243
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2019
Admission routes1
Has abstractyes

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