Gender-Specific Differences in Access to Polysomnography and Prevalence of Sleep Disorders
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that women have less access or longer waiting times to high-tech medicine compared with men. This study aimed to detect possible gender differences in access to the diagnostic high-tech method of polysomnography (PSG). Furthermore, the study explored gender differences in prevalence of specific sleep diseases. MATERIALS AND METHODS: Source data of n = 1000 patients, who underwent PSG at the Medical University of Innsbruck, were reviewed. Clinical data regarding time elapsed between symptom onset and PSG as well as final diagnoses were analyzed for gender differences. RESULTS: Six hundred sixty-nine men and 331 women were examined with PSG. There were no gender differences in access to PSG after first presentation to the sleep laboratory. Significantly more men than women (13.3 vs. 6.9%) were referred to medical examination because of abnormal observations by their bed partner. In men we found more sleep-related breathing disorders and fragmentary myoclonus, whereas in women insomnia was more common. Sleep-related breathing disorders showed a more severe manifestation in men, however, there was no difference in treatment with continuous positive airway pressure/biphasic positive airway pressure therapy between male and female patients. CONCLUSION: Twice as many men than women received a PSG. This is explained by the referral rates to the sleep laboratory. While there are well-established gender differences for some sleep disorders, the fact that twice as many men than women were referred to the sleep laboratory could indicate a lower awareness for sleep disorders in women.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".