The impact of cumulative partial sleep deprivation on simulated driving and cognitive functions: a randomized, controlled trial
Bibliographic record
Abstract
Sleep deprivation increases the odds of being involved in a road traffic accident. An estimated 23% of adults in Canada sleep at least one hour less than what they consider optimal during the work week. However, the impact of cumulative partial sleep deprivation of one hour per night for six nights on driving performance has not been studied. The present dissertation set to determine 1) the impact of cumulative partial sleep deprivation versus a placebo condition on driving performance; 2) if changes in sustained attention, working memory, response inhibition, and decision-making following sleep deprivation mediate the effect of cumulative partial sleep deprivation on driving performance; and 3) the extent to which age, sex, and chronotype moderate this impact. The samples included healthy participants (ages 18-25 and 30-34) who were randomly allocated to undergo either one-hour nightly sleep restriction or a placebo control condition. Sleep measures, cognitive performance, and driving simulator performance were measured at baseline and following experimental manipulation. The findings, presented in a series of three manuscript, are the following: 1) cumulative partial sleep deprivation did not impair driving performance; 2) cumulative partial sleep deprivation negatively affects performance on a test of working memory capacity, but does not affect performance on tests of sustained attention, response inhibition, or decision making; 3) impairment in working memory following cumulative partial sleep deprivation did not lead to an increase in lateral position variability; 4) corrected midpoints of sleep on free days (a measure of chronotype) derived from the Munich Chronotype Questionnaire and from actigraphy are on average the same; and 5) chronotype, sex, or age did not moderate the findings on cumulative partial sleep deprivation and lateral position variability and driving speed
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".