MétaCan
Menu
Back to cohort
Record W4404318338 · doi:10.1101/2024.11.12.24316879

Ventilatory Burden Predicts Change in Sleepiness Following Positive Airway Pressure in Sleep Apnea

2024· preprint· en· W4404318338 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood InstituteNational Health and Medical Research CouncilMedical Research CouncilNational Institutes of HealthSigrid Juséliuksen SäätiöNordForskBusiness FinlandKuopion Yliopistollinen SairaalaAmerican Heart AssociationQueensland HealthUniversity of QueenslandBrigham and Women's HospitalFlinders UniversityRespicardiaAmerican Academy of Sleep Medicine FoundationMassachusetts General HospitalAmerican Sleep Medicine FoundationUniversity of AlbertaSuomen KulttuurirahastoResMedMetro North Hospital and Health ServiceEli Lilly and Company
KeywordsSleep apneaAirwayApneaMedicineContinuous positive airway pressurePositive airway pressureAnesthesiaObstructive sleep apneaSleep (system call)Computer science

Abstract

fetched live from OpenAlex

Abstract Rationale Excessive daytime sleepiness, an important symptom of obstructive sleep apnea (OSA), is commonly quantified using the Epworth Sleepiness Scale score (ESS). Baseline OSA severity measures (ventilatory burden, flow limitation, and hypoxemia) provide insights into OSA pathophysiology and could predict changes in sleepiness (i.e. change-in-ESS) following continuous positive airway pressure (CPAP) treatment. Objectives We hypothesized that change-in-ESS following CPAP treatment can be predicted from baseline polysomnography. Methods Associations between OSA severity measures and ESS were evaluated in 2332 participants, adjusting for age, sex, BMI, and total sleep time. Change-in-ESS prediction was evaluated using 213 CPAP treatment studies (HomePAP, BestAIR, and ABC) in three steps: severity measures were compared (adjusted regression, n =64), a prediction model was developed using baseline ventilatory burden and baseline ESS ( n =139), and then evaluated in holdout participants ( n =74). Measurements and Main Results In cross-sectional analysis, ESS was associated with ventilatory burden (0.45 points/SD; 95% CI 0.23−0.67), hypoxic burden (0.39; 0.17−0.62), the apnea-hypopnea index (AHI) (0.36; 0.14−0.59), and flow limitation severity (0.22; 0.01−0.43). Comparison analysis revealed that change-in-ESS was most strongly associated with baseline ventilatory burden (-1.08 points/SD; -2.13 to -0.05) and baseline ESS (-2.75; -3.83 to -1.69); the AHI association was weaker (-0.97; -2.01−0.05). Predicted change-in-ESS and actual change-in-ESS were correlated in holdout participants (adjusted R² =0.313); median [IQR] actual change-in-ESS of predicted responders (≥2-point ESS improvement, n =54, 73.0%) was -5.0 [-10.0 to -2.0] and non-responders was 0.0 [-1.0−1.0] ( P <0.001). Conclusions Baseline ventilatory burden and baseline ESS were independently associated with change-in-ESS and could be used together to inform clinicians whether CPAP treatment will likely improve a patient’s sleepiness.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.004
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 head0.026
GPT teacher head0.316
Teacher spread0.289 · 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