O064 A Portable Ocular Assessment for Predicting Fitness to Drive under Extended-Wakefulness Conditions – Preliminary Analysis
Bibliographic record
Abstract
Abstract Introduction In response to the high incidence of fatigue-based vehicle accidents, roadside assessments of sleepiness are in significant demand. For this purpose, we’ve piloted the existing NeuroFlex® Platform, which takes ocular performance measurements of both prosaccade and antisaccade eye-movements using a portable Virtual Reality (VR) headset. We’ve conducted preliminary comparisons between these measures and simulated driving performance. Methods Sixteen young-adults (females= 8; age M= 25.13, SD= 4.30) completed five test batteries starting 1-hour post-wake where repeated testing encapsuled more than 24-hours of extended wakefulness. Each battery consisted of one 60-minute drive on the AusEd driving simulator, in addition to three administrations of 60-second prosaccade and antisaccade assessments using the NeuroFlex® VR platform. Results Steering deviations from the median lane position showed a time main effect, F(4,24.49) = 13.38, p <.001, with the most diminished performance occurring at 19 (M=59.50cm, SD=26.61cm) and 25-hours post-wake (M=72.88cm, SD= 40.09cm) (vs. peak performance @13-hours post-wake M=35.09, SD=12.81; M diff.= 24.41 and 37.79 respectively, p <.001). One moderate association between prosaccade latency and steering deviation was found in the final battery following 25-hours of wakefulness (r=.51, p= 0.04). No significant associations were found for antisaccade latency. Discussion Despite the lack of significant associations with steering deviations, eye-movement latency did demonstrate durational increases over extended wakefulness. In this preliminary investigation we were limited to the existing output generation of the NeuroFlex® platform. Therefore, with refined data analyses of ocular control, we see promise in the NeuroFlex® platforms capability of detecting road users’ fitness to drive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".