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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 teacher head, 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".