Simulator Performance vs. Neurophysiologic Monitoring: Which is More Relevant to Assess Driving Impairment?
Bibliographic record
Abstract
Previously, we reported on circadian variation in driving simulator performance and neurophysiologic evidence of sleep intrusion into consciousness in a pilot study of healthy individuals. We have since expanded this “normative” sample and run a prospective comparison study with a sample of clinical patients reporting excessive daytime sleepiness (EDS) as a chief complaint. Thirty healthy adults (mean age of 31.3 ± 11.5) and 27 EDS patients (47.0 ± 13.7) with valid driver’s licenses were included. Subjects performed four intentionally soporific 30-minute driving simulations at two-hour intervals while undergoing continuous EEG monitoring for microsleep (MS) episodes. Measured variables included: subjective ratings of sleepiness and alertness prior to each drive, lane position accuracy, mean speed, speed deviation, mean reaction time (RT) to “virtual” wind gusts as well as off-road events, i.e., “crashes.” In comparing normative individuals and EDS patients, significant between-group differences were found between subjective ratings, RT, crashes and MS. Both groups showed a significant a tendency towards RT slowing during afternoon drives, with this circadian effect appearing most pronounced for EDS patients. Significant between-group differences were also found on subjective ratings of sleepiness and alertness, although diurnal fluctuation of subjective sleepiness ratings was significant only for the EDS group. Objective EEG MS monitoring demonstrated escalating sleep intrusion with repeated drives in both groups, but particularly for the EDS group. Total crash rates were three times higher in EDS patients, with an increasing trend towards crash-proneness in the late afternoon. In summary, we found significantly impaired performance on some, though not all, driving parameters for EDS patients. While increased crash rate may be the most dramatic of these, slowing of RT was the most statistically robust. EEG monitoring was able to document increased propensity towards MS episodes in patients with EDS, which we suggest is causative in creating this impairment. It remains unclear whether a neurophysiologic or simulator approach captures impairment due to sleepiness with greater sensitivity and specificity. A hybrid approach combining data from both sources may be optimal, and also could be integrated in commercial vehicle use. We suggest that the need for a more accurate hospitalbased screening tool for assessment of driving impairment due to sleep disorders remains an important issue for physicians and legislators dealing with driving competency.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".