Reducing Motor-Vehicle Collisions, Costs, and Fatalities by Treating Obstructive Sleep Apnea Syndrome
Bibliographic record
Abstract
STUDY OBJECTIVES: Drivers suffering from obstructive sleep apnea syndrome (OSAS) have an increased risk for being involved in motor-vehicle collisions. This study estimates, for the first time, the annual OSAS-related collisions, costs, and fatalities in the United States and performs a cost-benefit analysis of treating drivers suffering from OSAS with continuous positive airway pressure (CPAP). DESIGN: The MEDLINE-PubMed database (1980 to 2003) was searched for information on OSAS. A meta-analysis was performed of studies investigating the relationship between collisions and OSAS. Data from the National Safety Council were used to estimate OSAS-related collisions, costs, and fatalities and their reduction with treatment. Next, the annual cost of treating OSAS with CPAP was calculated. Finally, multiple 1-way sensitivity analyses were performed. SETTING: N/A. PATIENTS OR PARTICIPANTS: N/A. INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: More than 800,000 drivers were involved in OSAS-related motor-vehicle collisions in the year 2000. These collisions cost 15.9 billion dollars and 1,400 lives in the year 2000. In the United States, treating all drivers suffering from OSAS with CPAP would cost 3.18 billion dollars, save 11.1 billion dollars in collision costs, and save 980 lives annually. CONCLUSION: Annually, a small but significant portion of motor-vehicle collisions, costs, and deaths are related to OSAS. With CPAP treatment, most of these collisions, costs, and deaths can be prevented. Treatment of OSAS benefits both the patient and the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".