Obstructive sleep apnea is associated with fatigue in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) patients often suffer from fatigue. OBJECTIVE: We evaluated the relationship of obstructive sleep apnea (OSA) to fatigue and sleepiness in MS patients. METHODS: Ambulatory MS patients without known sleep disorders and healthy controls underwent diagnostic polysomnography and a multiple sleep latency test (objective sleepiness measure). Fatigue was measured with the Fatigue Severity Scale (FSS) and the Multidimensional Fatigue Inventory (MFI), and subjective sleepiness by Epworth Sleepiness Scale. Covariates included age, sex, body mass index, Expanded Disability Status Scale (EDSS), depression, pain, nocturia, restless legs syndrome, and medication. RESULTS: OSA (apnea-hypopnea index ≥ 15) was found in 36 of 62 MS subjects and 15 of 32 controls. After adjusting for confounders, severe fatigue (FSS ≥ 5) and MFI-mental fatigue (>group median) were associated with OSA and respiratory-related arousals in MS, but not control subjects. Subjective and objective sleepiness were not related to OSA in either group. In a multivariate model, variables independently associated with severe fatigue in MS were severe OSA [OR 17.33, 95% CI 2.53-199.84], EDSS [OR 1.88, 95% CI 1.21-3.25], and immunomodulating treatment [OR 0.14, 95% CI 0.023-0.65]. CONCLUSIONS: OSA was frequent in MS and was associated with fatigue but not sleepiness, independent of MS-related disability and other covariates.
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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 | 0.000 | 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".