Assessment of Obstructive Sleep Apnea Among Patients With Chronic Obstructive Pulmonary Disease in Primary Care
Bibliographic record
Abstract
Study Objectives: Observational studies link untreated obstructive sleep apnea (OSA) with adverse outcomes in chronic obstructive pulmonary disease (COPD). The first step in addressing OSA is a clinical assessment. However, given competing demands and a lack of high-quality evidence, it is unclear how often such assessments occur. We explored the documentation of OSA assessment among patients with COPD in primary care, and the patient and provider characteristics associated with these assessments. Methods: We conducted a cross-sectional study of patients with clinically diagnosed COPD at 2 primary care practices. We abstracted charts to determine whether providers assessed OSA, defined as documentation of symptoms, treatment, or a referral to sleep medicine. We performed multivariable mixed-effects logistic regression to assess the associations of patient and provider characteristics with OSA assessment. Results: Among 641 patients with clinically diagnosed COPD, 146 (23%) had OSA assessed over a 1-year period. Positive associations with OSA assessment included body mass index ≥ 30 (odds ratio [OR] 3.5, 95% confidence interval [CI] 1.8-7.0), pulmonary subspecialist visits (OR 3.9, 95%CI 2.4-6.3), and a prior sleep study demonstrating OSA documented within the electronic medical record (OR 18.0, 95%CI 9.0-35.8). Notably, patients identifying as Black were less likely to have OSA assessed than those identifying as White (OR 0.5, 95%CI 0.2-0.9). Conclusions: Providers document an assessment of OSA among a quarter of patients with COPD. Our findings highlight the importance of future work to rigorously test the impact of assessment on important health outcomes. Our findings also reinforce that additional strategies are needed to improve the equitable delivery of care.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".