Prevalence of Sleep-disordered Breathing in Obese Patients with Chronic Hypoxemia. A Cross-Sectional Study
Bibliographic record
Abstract
RATIONALE: Hypoxemia in obese patients is likely to be associated with a high prevalence of sleep-disordered breathing. Supplemental oxygen is commonly used to treat chronic hypoxemia but carries some risk in obese individuals due to unrecognized comorbid obstructive sleep apnea (OSA) and obesity hypoventilation syndrome (OHS). OBJECTIVES: The first step in the estimation of this risk is to determine the prevalence of OSA and OHS in obese individuals with chronic, awake hypoxemia. METHODS: A single-center retrospective cohort study was performed to assess the prevalence and severity of OSA and OHS among obese individuals with hypoxemia. One hundred eighty-four individuals underwent arterial blood gas testing and polysomnography. One hundred fifty-eight of these individuals also had spirometry. MEASUREMENTS AND MAIN RESULTS: The prevalence of OSA was 80%, and the prevalence of OHS was 51%. Chronic obstructive pulmonary disease (COPD) was confirmed by spirometry in 49% of the cohort, and OSA was found in 69% of those individuals. The severity of hypoxemia in this cohort was not statistically related to COPD, OSA, or OHS. CONCLUSIONS: OSA and OHS are highly prevalent in obese patients with chronic awake hypoxemia, and OSA frequently coexists with COPD. Evaluation of chronic, awake hypoxemia solely based on arterial blood gas measurements and pulmonary function testing is not sufficient to identify OSA and OHS. Further diagnostic sleep testing should be performed to identify those who could benefit from alternative therapies and to avoid potential harm from treatment with supplemental oxygen alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".