The effect of cpap therapy on blood pressure in the mild obstructive sleep apnea population
Bibliographic record
Abstract
This retrospective study uniquely focused on the effects of continuous positive airway pressure (CPAP) on blood pressure in patients with mild obstructive sleep apnea (OSA), a condition characterized by intermittent upper airway collapse. OSA is a well-established risk factor for hypertension, with positive airway pressure (PAP) therapy being the most common treatment. While substantial evidence supports PAP therapy in moderate and severe OSA, the impact of CPAP on mild OSA, which accounts for 50%-70% of all cases, remains underexplored. The primary objective of this study was to investigate whether CPAP therapy, particularly the degree of adherence to treatment, has a measurable impact on blood pressure regulation in patients with mild OSA. Specifically, this research aimed to evaluate whether consistent CPAP use could lower systolic and diastolic blood pressure and assess how varying levels of adherence influence these outcomes. Participants were stratified into two groups based on CPAP adherence: Group A (n = 45) included those who used CPAP for four or more hours per night on at least 70% of nights, while Group B (n = 15) consisted of those with less than four hours of CPAP use per night on 70% of nights. Interestingly, greater systolic and diastolic blood pressure reductions were observed in Group B, suggesting that lower CPAP adherence may still contribute to significant cardiovascular improvements. These findings offer new insights into the management of blood pressure in patients with mild OSA and the role of CPAP adherence.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".