Loud Snoring is A Risk Factor for Occupational Injury in Farmers
Bibliographic record
Abstract
BACKGROUND: Loud snoring is a common symptom in the general population. The evidence-based literature indicates that snoring may be associated with sleep fragmentation and sleep apnea, which may affect cognitive function and predispose to occupational injury. High rates of occupational injury occur on farms and may be related to personal and health factors. Thus, loud snoring may not be a trivial symptom and should be considered as important in medical assessments. METHODS: A prospective cohort study was conducted in Saskatchewan. Baseline questionnaires were completed for 5502 individuals by representatives from 2390 farms. Sleep patterns at baseline were categorized as the following: no reported sleep disorders; physician-diagnosed sleep apnea (treatment unknown); and loud snoring. Survival analyses were used to relate sleep patterns with subsequent injury. RESULTS: A total of 6.7% (369 of 5502) of participants reported a possible sleep disorder. Of these, 69.4% (256 of 369) reported loud snoring only. Loud snoring was only associated with a consistent increase in risk (eg, HR 1.45 [95 CI 1.07 to 1.99 for work-related injury]) for five farm injury outcomes. Relationships between physician-diagnosed sleep apnea and time to first injury were not significant, presumably because a diagnosis of sleep apnea implied treatment for sleep apnea. DISCUSSION: Sleep disorders are an important potential risk factor for occupational injury on farms. Substantial proportions of farm residents report loud snoring and this is related to subsequent injury. Some of these cases may represent sleep fragmentation or undiagnosed obstructive sleep apnea. Identification and clinical management of sleep disorders related to snoring should be part of health assessments conducted by physicians.
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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 | 0.001 |
| 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 | 0.001 |
| 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".