Poorer objective but not subjective driving performance in drivers vulnerable to sleep loss effects during extended wake
Bibliographic record
Abstract
Sleepiness-related errors are a leading cause of driving accidents, requiring drivers to effectively monitor sleepiness levels. However, there are inter-individual differences in driving performance after sleep loss, with some showing poor driving performance while others show minimal impairment. This research explored if there are differences in self-reported sleepiness and driving performance in healthy drivers who exhibited vulnerability or resistance to objective driving impairment following extended wakefulness. Thirty-two adults (female = 18, mean age = 33.0 ± 14.6 years) completed five × 60-min simulated drives across 29-hr of extended wakefulness. Subjective sleepiness (Karolinska Sleepiness Scale) and subjective driving performance ratings (nine-point Likert scale) were assessed at 10-min intervals while driving. Cluster analysis using simulator steering deviation and crash data categorised participants as vulnerable (n = 16) or resistant (n = 16) to driving impairments following extended wakefulness. No differences in self-ratings between the vulnerable and resistant groups were observed except during the last drive (25 hr awake), where the vulnerable group reported higher sleepiness (p = 0.008) and worse driving performance (p = 0.001) than the resistant group. For each 1-point increase on the Karolinska Sleepiness Scale and subjective driving scales, the vulnerable group showed about threefold greater steering impairment relative to resistant drivers. Although self-reported sleepiness and driving performance were correlated with objective driving performance, vulnerable drivers reported similar sleepiness and driving performance as resistant drivers. Thus, self-reported sleepiness and driving performance are not reliably sensitive to sleep loss effects on objective driving performance, which may impact the vulnerable driver's decisions to continue driving and delay engagement in countermeasures to reduce crash risk (e.g. napping), warranting further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".