0464 OBSTRUCTIVE SLEEP APNEA AND RISK OF OCCUPATIONAL INJURY
Bibliographic record
Abstract
To investigate whether patients with obstructive sleep apnea (OSA) are at increased risk of occupational injury (OI) Patients referred to the University of British Columbia (BC) Hospital Sleep Laboratory for suspected OSA (May 2003 to July 2011) were recruited and those diagnosed with OSA using polysomnography (PSG) were included in the analyses. Information from patients with OSA (AHI greater than 5/hr) were linked with the workers’ compensation claims database to identify OI resulting in at least one day off work in the five years prior to PSG. The odds of injury in each year was compared to a matched control group (by age, gender, industry type) taken from the general population of BC. Logistic regression was used to model the odds of a work-related injury over the five-year period. A total of 872 patients with OSA and 4360 controls were included in the study. There were 128 OI in the OSA patients (3.1% had a least one OI) and 791 OI in the control population (3.7% of controls had at least one OI). In the logistic regression model, OSA was not associated with an increased odds of OI (OR = 0.84, 95% CI = 0.68–1.03, p = 0.10). This association remained unchanged in the model adjusted for the confounding effects of age and gender (OR = 0.84, CI = 0.68–1.03, p= 0.10). In a secondary analysis restricted to injuries potentially associated with vigilance at work, patients with OSA had a similar rate of OI as matched controls (OR=1.00, CI=0.73–1.37, p= 0.99). In this matched analysis, OSA was not associated with an increased risk of OI. Whether this is due to the lack of impact of OSA in the period prior to diagnoses, or other unknown confounders (e.g. work time, job descriptions) is open to discussion. CIHR Sleep Team Grant.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".