Sleep disorders as a cause of motor vehicle collisions.
Bibliographic record
Abstract
Studies have shown that a large proportion of traffic accidents around the world are related to inadequate or disordered sleep. Recent surveys have linked driver fatigue to 16% to 20% of serious highway accidents in the UK, Australia, and Brazil. Fatigue as a result of sleep disorders (especially obstructive sleep apnea), excessive workload and lack of physical and mental rest, have been shown to be major contributing factors in motor vehicle accidents. A number of behavioral, physiological, and psychometric tests are being used increasingly to evaluate the impact of fatigue on driver performance. These include the oculography, polysomnography, actigraphy, the maintenance of wakefulness test, and others. Various strategies have been proposed for preventing or reducing the impact of fatigue on motor vehicle accidents. These have included: Educational programs emphasizing the importance of restorative sleep and the need for drivers to recognize the presence of fatigue symptoms, and to determine when to stop to sleep; The use of exercise to increase alertness and to promote restorative sleep; The use of substances or drugs to promote sleep or alertness (i.e. caffeine, modafinil, melatonin and others), as well as specific sleep disorders treatment; The use of CPAP therapy for reducing excessive sleepiness among drivers who have been diagnosed with obstructive sleep apnea. The evidence cited in this review justifies the call for all efforts to be undertaken that may increase awareness of inadequate sleep as a cause of traffic accidents. It is strongly recommended that, for the purpose of promoting highway safety and saving lives, all disorders that cause excessive sleepiness should be investigated and monitored.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 0.001 |
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".