Sleep-disordered breathing is associated with brain vascular reactivity in spinal cord injury
Bibliographic record
Abstract
Objective To determine the population-level odds of individuals with spinal cord injury (SCI) experiencing fatigue and sleep apnea, to elucidate relationships with level and severity of injury, and to examine associations with abnormal cerebrovascular responsiveness. Methods We used population-level data, meta-analyses, and primary physiologic assessments to provide a large-scale integrated assessment of sleep-related complications after SCI. Population-level and meta-analyses included more than 60,000 able-bodied individuals and more than 1,800 individuals with SCI. Physiologic assessments were completed on a homogenous sample of individuals with cervical SCI and matched controls. We examined the prevalence of (1) self-reported chronic fatigue, (2) clinically identified sleep apnea, and 3) cerebrovascular responsiveness to changing CO2. Results Logistic regression revealed a 7-fold elevated odds of chronic fatigue after SCI (odds ratio [OR] 7.9, 95% confidence interval [CI] 3.5–16.2), and that fatigue and trouble sleeping are correlated with the level and severity of injury. We further show that those with SCI experience elevated risk of clinically defined sleep-disordered breathing in more than 600 individuals with SCI (pooled OR 3.1, 95% CI 1.3–7.5). We confirmed that individuals with SCI experience a high rate of clinically defined sleep apnea using primary polysomnography assessments. We then provide evidence using syndromic analysis that sleep-disordered breathing is a factor strongly associated with impaired cerebrovascular responsiveness to CO2 in patients with SCI. Conclusions Individuals with SCI have an increased prevalence of sleep-disordered breathing, which may partially underpin their increased risk of stroke. There is thus a need to integrate sleep-related breathing examinations into routine care for individuals with SCI.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".