Risk and severity of motor vehicle crashes in patients with obstructive sleep apnoea/hypopnoea
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnoea/hypopnoea (OSAH) appears to be associated with an increased risk of motor vehicle crashes (MVCs). However, its impact on crash patterns, particularly the severity of crashes, has not been well described. A study was undertaken to determine whether OSAH severity influenced crash severity in patients referred for investigation of suspected sleep-disordered breathing. METHODS: Objective crash data (including the nature of crashes) for 783 patients with suspected OSAH for the 3 years prior to polysomnography were obtained from provincial insurance records and compared with data for 783 age- and sex-matched controls. The patient group was 71% male with a mean age of 50 years, a mean apnoea-hypopnoea index (AHI) of 22 events/h and a mean Epworth Sleepiness Scale score of 10. RESULTS: There were 375 crashes in the 3-year period, 252 in patients and 123 in controls. Compared with controls, patients with mild, moderate and severe OSAH had an increased rate of MVCs with relative risks of 2.6 (95% CI 1.7 to 3.9), 1.9 (95% CI 1.2 to 2.8) and 2.0 (95% CI 1.4 to 3.0), respectively. Patients with suspected OSAH and normal polysomnography (AHI 0-5) did not have an increased rate of MVC (relative risk 1.5 (95% CI 0.9 to 2.5), p = 0.21). When the impact of OSAH on MVC associated with personal injury was examined, patients with mild, moderate and severe OSAH had a substantially higher rate of MVCs than controls with relative risks of 4.8 (95% CI 1.8 to 12.4), 3.0 (95% CI 1.3 to 7.0) and 4.3 (95% CI 1.8 to 8.9), respectively, whereas patients without OSAH had similar crash rates to controls with a relative risk of 0.6 (95% CI 0.2 to 2.5). Very severe MVCs (head-on collisions or those involving pedestrians or cyclists) were rare, but 80% of these occurred in patients with OSAH (p = 0.06). CONCLUSION: Patients with OSAH have increased rates of MVCs, and disproportionately increased rates of MVCs are associated with personal injury.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".