The challenge of identifying family medicine patients with obstructive sleep apnea: addressing the question of gender inequality
Bibliographic record
Abstract
Purpose: The purpose of this study was to examine the sleep characteristics, metabolic syndrome disease and likelihood of obstructive sleep apnea in a sample of older, family medicine patients previously unsuspected for sleep apnea. Methods: A total of 295 participants, minimum age 45, 58.7% women, were recruited from two family medicine clinics. None previously had been referred for sleep apnea testing. All participants completed a sleep symptom questionnaire and were offered an overnight polysomnography study, regardless of questionnaire results. 171 followed through with the sleep laboratory component of the study. Health data regarding metabolic syndrome disease (hypertension, hyperlipidemia, diabetes and obesity) were gathered by chart review. Results: Overall, more women than men enrolled in the study and pursued laboratory testing. Of those who underwent polysomnography testing, 75% of the women and 85% of the men were diagnosed with sleep apnea based on an apnea/hypopnea index of 10 or greater. Women and men had similar polysomnography indices, the majority being in the moderate to severe ranges. In those with OSA diagnosis, gender differences in sleep symptom severity were not significant. Conclusions: We conclude that greater gender equality in sleep apnea rates can be achieved in family practice if sleep apnea assessments are widely offered to older patients.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".