O078 A Comparison of Subjective Sleepiness and Subjective driving Performance between People Vulnerable Versus Resistant to Driving Impairment following Extended Wakefulness.
Bibliographic record
Abstract
Abstract Introduction Subjective sleepiness and driving performance are generally associated but show inter-individual variability. Specifically, some people are more vulnerable, while others are more resistant to driving impairment during extended wakefulness. We examined the relationship between subjective sleepiness and driving performance in groups resistant versus vulnerable to driving impairment during extended wakefulness. Methods Thirty-two adults (female=18, mean age=33.0yrs, SD=14.6) completed five 60-minute driving simulator assessments across 29 hours of extended wakefulness. Perceived sleepiness (Karolinska sleepiness scale, KSS) and driving performance (nine-point Likert scale) were assessed at 10-minute intervals while driving. Through cluster analysis, participants were categorised as vulnerable (n=16) or resistant (n=16) using steering deviation and crash data. Correlations, stepwise regressions, and ROC curves were used to identify predictors of driving impairment. Results Perceived sleepiness and driving impairment increased across the drives during wakefulness and within drives, regardless of grouping (p<0.001). The exception was the drive at 25-hours into wakefulness, where the vulnerable group showed higher perceived driving impairment within the drive (p=0.001). Pre-drive KSS, total sleep time, age and gender were not significant predictors of crashes at drives undertaken at 1-hour, 7-hours, 13-hours, or 25-hours, but were significant at 19-hours into wakefulness, together explaining 44% of the variance in crashes. Discussion Self-reports are sensitive to driving impairment but not differential vulnerability to performance decrements during extended wakefulness. However, the findings support that both groups can perceive their sleepiness and ideally employ appropriate countermeasures (e.g., stop driving, nap, caffeine). Future studies should target more objective predictors of vulnerable versus resistant groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".