Sex Differences in Obstructive Sleep Apnea after Stroke
Bibliographic record
Abstract
ABSTRACT: Background and Objectives: Obstructive sleep apnea (OSA) is prevalent after stroke and associated with recurrent stroke, prolonged hospitalization, and decreased functional recovery. Sex differences in post-stroke OSA remain underexplored. The objective of this study was to evaluate sex differences in functional outcomes, stroke and OSA severity, and clinical manifestations of OSA in stroke patients with OSA. Methods: We retrospectively evaluated data from three previously conducted studies. Study patients had an imaging-confirmed stroke and had been found to have OSA (apnea–hypopnea index [AHI] ≥ 5) on either in-laboratory polysomnography or home sleep apnea testing performed within 1 year of their stroke. Linear regression models were used to evaluate study outcomes. Results: In total, 171 participants with post-stroke OSA (117 males [68.4%] and 54 females [31.6%]) were included. Female sex was an independent predictor for greater functional impairment (β = 0.37, 95% CI 0.029–0.71, p = 0.03), increased stroke severity (β = 1.009, 95% CI 0.032–1.99, p = 0.04), and greater post-stroke depressive symptoms (β = 3.73, 95% CI 0.16–7.29, p = 0.04). Female sex was associated with lower OSA severity, as measured by the AHI (β = –5.93, 95% CI –11.21– –0.66). Sex was not an independent predictor of specific symptoms of OSA such as daytime sleepiness, snoring, tiredness, and observed apneas. Conclusion: Females with post-stroke OSA had poorer functional outcomes and more severe strokes compared to males, despite having lower OSA severity. Females with post-stroke OSA also exhibited more depressive symptoms. Understanding sex differences in patients with post-stroke OSA will likely facilitate better recognition of OSA and potentially improve clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".