Trials in Sleep Apnea and Stroke
Bibliographic record
Abstract
Few randomized controlled trials have evaluated the effectiveness of continuous positive airway pressure (CPAP) in reducing recurrent vascular events and mortality in poststroke obstructive sleep apnea (OSA). To date, results have been mixed, most studies were underpowered and definitive conclusions are not available. Using lessons learned from prior negative trials in stroke, we reappraise prior randomized controlled trials that examined the use of CPAP in treating poststroke OSA and propose the following considerations: (1) Intervention-based changes, such as ensuring that patients are using CPAP for at least 4 hours per night (eg, through use of improvements in CPAP technology that make it easier for patients to use), as well as considering alternative treatment strategies for poststroke OSA; (2) Population-based changes (ie, including stroke patients with severe and symptomatic OSA and CPAP noncompliers); and (3) Changes to timing of intervention and follow-up (ie, early initiation of CPAP therapy within the first 48 hours of stroke and long-term follow-up calculated in accordance with sample size to ensure adequate power). Given the burden of vascular morbidity and mortality in stroke patients with OSA, there is a strong need to learn from past negative trials and explore innovative stroke prevention strategies to improve stroke-free survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".