Sleep Disorders in a Military Population
Bibliographic record
Abstract
INTRODUCTION: Sleep disorders are common in the civilian population, but little is known about which sleep disorders are common in members of the military. This article compares a group of military personnel referred to our sleep disorders center with a group of civilian controls also referred to our sleep disorders center. METHODS: We analyzed the data of 70 Canadian military personnel and 70 civilian controls matched for age and gender. All subjects had full polysomnography. We compared reasons for referral and final sleep diagnoses for both groups. RESULTS: The mean age of each group was 40.8 +/- 7.0 SD (military) and 40.8 +/- 7.3 SD (civilians), and there were 61 men and 9 women in each group. Both groups were obese (body mass index, 30.2 +/- 5.3 (military) versus 32.5 +/- 6.9 (civilian)). Both groups were also pathologically sleepy during the day (Epworth Sleepiness Score, 10.4 +/- 4.6 (military) versus 11.3 +/- 5.4 (civilian)). The majority of referrals in each group were to rule out a sleep breathing disorder (SBD) (66% military versus 79% civilian, p = not significant). Only military patients were referred to rule out a movement disorder (17.1% military versus 0% civilian; 95% confidence interval of the difference = 8.4%-27.6%, p < 0.05). Fewer military were referred because of excessive daytime sleepiness or insomnia (7.1% military versus 20.0% civilian, 95% confidence interval of the difference = -24.4% to -1.4%, p < 0.05). The most common diagnosis confirmed in both groups was a SBD (53% military, 66% civilian, p = not significant). CONCLUSIONS: The range and distribution of sleep disorders seen in the military population is similar to that in the civilian population. Both groups were overweight and sleepy and were found to have SBD and movement disorders. These findings underscore the importance of diagnosing and treating sleep disorders in both groups. The neurocognitive impairment associated with SBD and movement disorders impacts highly on the ability of these groups to safely perform their jobs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".