Reduction in motor vehicle collisions following treatment of sleep apnoea with nasal CPAP
Bibliographic record
Abstract
BACKGROUND: Patients with untreated obstructive sleep apnoea (OSA) have increased motor vehicle collisions (MVCs). When successfully treated, they report improved driving and fewer mishaps, but there are few objective data to confirm this. A study was therefore undertaken to examine actual MVC data in a large group of patients with OSA before and after treatment with continuous positive airway pressure (CPAP) compared with a control group matched for age, sex, and type of driver's licence (commercial or non-commercial). METHODS: Two hundred and ten patients of mean (SD) age 52 (11) years, body mass index (BMI) 35.5 (10) kg/m(2), apnoea/hypopnoea index (AHI) 54 (29) events/h were treated with CPAP for at least 3 years. MVC records were obtained from the Ontario Ministry of Transportation (MTO) database for patients and an equal number of randomly selected control drivers. MVC rates were compared for 3 years before and after CPAP therapy for patients and for the corresponding time frames for controls. RESULTS: Untreated patients with OSA had more MVCs than controls (mean (SD) MVCs/driver/year 0.18 (0.29) v 0.06 (0.17), p<0.001). Following CPAP treatment the number of MVCs/driver/year fell to normal (0.06 (0.17)) while, in controls, the MVC rate was unchanged over time (0.06 (0.17) v 0.07 (0.18), p=NS). Thus, the change in MVCs over time between the groups was very significant (change = -0.12 (95% CI -0.17 to -0.06), p<0.001)). The MVC rate in untreated patients (n=27) remained high over time. Driving exposure was not different following CPAP. CONCLUSIONS: The risk of MVCs due to OSA is removed when patients are treated with CPAP. As such, any restrictions on driving because of OSA could be safely removed after treatment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".