Elevated Risk of Sleepiness-Related Motor Vehicle Accidents in Patients With Obstructive Sleep Apnea Syndrome: A Case-Control Study
Bibliographic record
Abstract
OBJECTIVES: The present case-control study aimed to determine whether obstructive sleep apnea syndrome (OSAS) patients are at an increased risk for sleepiness-related motor vehicle accidents (MVAs) than controls and to identify disease-related factors associated with accident risk. METHODS: Demographic, anthropometric, clinical, and polysomnographic parameters of 312 OSAS patients were compared with 156 age- and sex-matched primary snoring subjects. RESULTS: The rate of OSAS patients reporting accident was higher than snoring subjects (21.2% vs. 11.5%, P = .011), and OSAS was associated with an increase in accident risk (odds ratio = 2.06, 95% confidence interval [CI], 1.17 to 3.61, P = .012). Younger OSAS patients (P = .001) and those who were male (P = .001), had greater neck circumference (P = .002), had a higher Epworth sleepiness score (ESS; P < .0001), and had a higher apnea-hypopnea index (AHI; p = .039) had more MVAs than OSAS patients. Daytime sleepiness was associated with a 2.74-fold increase (95% CI, 1.54 to 4.87, P = .001) in accident risk. In multiple logistic regression analysis, accident risk was associated with neck circumference (P < .031) and ESS (P < .0001). In addition, accident risk could be excluded in OSAS patients with neck circumference < 43 cm and ESS < 11 (sensitivity 33.3%, specificity 85.8%). CONCLUSIONS: The present results show that OSAS patients have a twofold higher risk of traffic accidents than control subjects, and increased neck circumference and excessive daytime sleepiness are useful in predicting OSAS patients at higher risk of having accidents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".