Sleep-Disordered Breathing Is Associated With Recurrent Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose- Limited data are available about the relationship between sleep-disordered breathing (SDB) and recurrent stroke and mortality, especially from population-based studies, large samples, or ethnically diverse populations. Methods- In the BASIC project (Brain Attack Surveillance in Corpus Christ), we identified patients with ischemic stroke (2010-2015). Subjects were offered screening for SDB with the ApneaLink Plus device, from which a respiratory event index (REI) score ≥10 defined SDB. Demographics and baseline characteristics were determined from chart review and interview. Recurrent ischemic stroke was identified through active and passive surveillance. Cause-specific proportional hazards models were used to assess the association between REI (modeled linearly) and ischemic stroke recurrence (as the event of interest), and all-cause poststroke mortality, adjusted for multiple potential confounders. Results- Among 842 subjects, the median age was 65 (interquartile range, 57-76), 47% were female, and 58% were Mexican American. The median REI score was 14 (interquartile range, 6-26); 63% had SDB. SDB was associated with male sex, Mexican American ethnicity, being insured, nonsmoking status, diabetes mellitus, hypertension, lower educational attainment, and higher body mass index. Among Mexican American and non-Hispanic whites, 85 (11%) ischemic recurrent strokes and 104 (13%) deaths occurred, with a median follow-up time of 591 days. In fully adjusted models, REI was associated with recurrent ischemic stroke (hazard ratio, 1.02 [hazard ratio for one-unit higher REI score, 95% CI, 1.01-1.03]), but not with mortality alone (hazard ratio, 1.00 [95% CI, 0.99-1.02]). Conclusions- Results from this large population-based study show that SDB is associated with recurrent ischemic stroke, but not mortality. SDB may therefore represent an important modifiable risk factor for poor stroke 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".