Simulated driving performance following prolonged wakefulness and alcohol consumption: separate and combined contributions to impairment
Bibliographic record
Abstract
The separate and combined effects of prolonged wakefulness and alcohol were compared on measures of subjective sleepiness, simulated driving performance and drivers' ability to judge impairment. Twenty-two males aged between 19 and 35 years were tested on four occasions. Subjects drove for 30 min on a simulated driving task under conditions determined by the factorial combination of 16 and 20 h of wakefulness and blood alcohol concentrations of 0.00 and 0.08%. The simulated driving session took place 30 min postingestion; subjects in the two alcohol conditions participated in a second 30-min driving session 90-min postingestion. Subjects made simultaneous ratings of their impairment while driving and retrospective ratings at the end of each test session. Subjective sleepiness measures were completed before and after each driving session. The combination of 20 h of prolonged wakefulness and alcohol produced significantly lower ratings of subjective sleepiness and driving performance that was worse, but not significantly so, than would be expected from the additive effects of each condition alone. Driving performance was always worse in the second driving session, during the elimination phase of alcohol metabolism, despite blood alcohol concentrations being lower than during the first driving session. There was a modest association between perceived and actual impairments in driving performance following prolonged wakefulness and alcohol. The findings suggest that the combination of prolonged wakefulness and alcohol consumption produced greater decrements in simulated driving performance than each condition alone and that drivers have only a modest ability to appreciate the magnitude of their impairment.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".