EAN/ERS/ESO/ESRS statement on the impact of sleep disorders on risk and outcome of stroke
Bibliographic record
Abstract
BACKGROUND: Sleep disorders are highly prevalent in the general population and may be linked in a bidirectional fashion to stroke, which is one of the leading causes of morbidity and mortality. AIM: Four major scientific societies established a task force of experts in neurology, stroke, respiratory medicine, sleep medicine and methodology to critically evaluate the evidence regarding potential links and the impact of therapy. MATERIALS AND METHODS: Thirteen research questions were evaluated in a systematic literature search using a stepwise hierarchical approach: first, systematic reviews and meta-analyses; second, primary studies post-dating the systematic reviews/meta-analyses. A total of 445 studies were evaluated and 88 were included. Statements were generated regarding current evidence and clinical practice. RESULTS: Severe obstructive sleep apnoea (OSA) doubles the risk for incident stroke, especially in young to middle-aged patients. Continuous positive airway pressure (CPAP) may reduce stroke risk, especially in treatment-compliant patients. The prevalence of OSA is high in stroke patients and can be assessed by polygraphy. Severe OSA is a risk factor for recurrence of stroke and may be associated with stroke mortality, whilst CPAP may improve stroke outcome. It is not clear if insomnia increases stroke risk, whilst the pharmacotherapy of insomnia may increase it. Periodic limb movements in sleep (PLMS), but not restless limb syndrome (RLS), may be associated with an increased risk of stroke. Preliminary data suggest a high frequency of post-stroke insomnia and RLS and their association with a less favourable stroke outcome, whilst treatment data are scarce. DISCUSSION/CONCLUSION: Overall, the evidence base is best for OSA relationship with stroke and supports active diagnosis and therapy. Research gaps remain especially regarding insomnia and RLS/PLMS relationships with stroke.
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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 | 0.075 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".