Insomnia, hypnotic use, and road collisions: a population-based, 5-year cohort study
Bibliographic record
Abstract
STUDY OBJECTIVES: The study objectives were to examine accidental risks associated with insomnia or hypnotic medications, and how these risk factors interact with sex and age. METHODS: A population-based sample of 3,413 adults (Mage = 49.0 years old; 61.5% female), with or without insomnia, were surveyed annually for five consecutive years about their sleep patterns, sleep medication usage, and road collisions. RESULTS: There was a significant risk of reporting road collisions associated with insomnia (hazard ratio [HR] = 1.20; 95% confidence interval [CI] = 1.00-1.45) and daytime fatigue (HR = 1.21; 95% CI = 1.01-1.47). Insomnia and its daytime consequences were perceived to have played some contributory role in 40% of the reported collisions. Both chronic (HR = 1.50; 95% CI = 1.17-1.91) and regular use of sleep medications (HR = 1.58; 95% CI = 1.16-2.14) were associated with higher accidental risks, as well as being young female with insomnia and reporting excessive daytime sleepiness. CONCLUSIONS: Both insomnia and use of sleep medications are associated with significant risks of road collisions, possibly because of or in association with some of their residual daytime consequences (i.e. fatigue and poor concentration). The findings also highlight a new group of at-risk patients, i.e. young women reporting insomnia and excessive daytime sleepiness.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".