Gender Differences in Obstructive Sleep Apnea with Comorbid Treatment-Resistant Depression
Bibliographic record
Abstract
Abstract Background. A bidirectional relationship between major depression and obstructive sleep apnea has been established, suggesting the possibility of overlapping and compounding disease processes. Depression, however, while more prevalent in women, is a highly heterogeneous disorder and can be difficult to treat regardless of gender. A common overlapping symptom of depression and obstructive sleep apnea is fatigue. Gender differences in obstructive sleep apnea symptomatology (and fatigue in particular) are also consistently observed. Here, we investigate obstructive sleep apnea in specific relation to treatment-resistant depression. Methods. A cross-sectional design was used to analyse data from 94 patients with treatment-resistant depression from a subspecialist mood disorders outpatient service who had no previous sleep assessment. Participants completed overnight polysomnography and a battery of rating scales assessing mood, sleep, and daytime functioning. Linear regression models determined whether presence of fatigue in treatment-resistant depression predicted sleep obstructive apnea severity. Results. There was a high prevalence of previously undiagnosed obstructive sleep apnea in our sample of patients with treatment-resistant depression. Treatment-resistant depression closed the gap in obstructive sleep apnea prevalence between men and women. Conclusions. We argue that typical symptoms of treatment-resistant depression may overshadow key symptoms of undetected obstructive sleep apnea. Specifically, we found that daytime fatigue masked a potentially significant underlying sleep disorder in women only. Comprehensive assessment and screening for sleep apnea in patients with treatment-resistant is encouraged, and the importance of investigating severity of fatigue in this population is emphasized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".