Dissociation of Obstructive Sleep Apnea From Hypersomnolence and Obesity in Patients With Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Obstructive sleep apnea (OSA) is seldom considered in the diagnostic investigation in the poststroke period although it is a stroke risk factor and has adverse prognostic implications after stroke. One reason might be that widely used clinical criteria for detection of OSA in the general community are not applicable in patients with stroke. We hypothesized that patients with stroke report less sleepiness and are less obese than subjects from a community sample with the same severity of OSA. METHODS: We performed polysomnography in 96 consecutive patients with stroke admitted to a stroke rehabilitation unit and in a community sample of 1093 subjects without a history of stroke. We compared the degrees of subjective sleepiness assessed by the Epworth Sleepiness Scale and body mass index between the 2 samples according to OSA categories assessed by the frequency of apneas and hypopneas per hour of sleep (<5, no OSA; 5 to <15 mild OSA; and >or=15, moderate to severe OSA). RESULTS: Compared with the community sample, patients with stroke with OSA had significantly lower Epworth Sleepiness Scale scores and body mass index for mild OSA (Epworth Sleepiness Scale 9.3+/-0.3 versus 5.6+/-0.5, P<0.001 and body mass index 33.1+/-0.5 versus 28.5+/-1.1, P<0.048) and for moderate to severe OSA (Epworth Sleepiness Scale 9.7+/-0.4 versus 7.1+/-0.9, P=0.043 and body mass index 36.4+/-0.8 versus 27.2+/-0.8 kg/m(2), P<0.025). CONCLUSIONS: For a given severity of OSA, patients with stroke had less daytime sleepiness and lower body mass index than subjects without stroke. These factors may make the diagnosis of OSA elusive in the poststroke period and preclude many such patients from the potential benefits of OSA therapy.
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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 | 0.003 |
| 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 | 0.001 | 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".