The utility of cognitive testing to predict real world commercial driving risk
Bibliographic record
Abstract
Driving is a complex task which requires numerous cognitive and sensorimotor skills to be performed safely. On-road driver evaluation can identify unsafe drivers but can also be expensive, risky, and time-consuming. Poor performance on off-road measures of cognition and sensorimotor control has been shown to predict on-road performance in privately-licensed light vehicle drivers, but commercial drivers have not yet been studied despite such vehicles generally being larger and heavier, thus increasing risks from unsafe driving. Commercially-licensed truck, bus, and light vehicle drivers undertook the tablet-based Vitals cognitive screening tool, which measures reaction time, judgement, memory, and sensorimotor control, and also undertook an on-road driving evaluation using their vehicle. Accuracy and reliability of the Vitals tasks on predicting road test outcomes were investigated using a trichotomous classifier (pass, fail, borderline), and task performance was analyzed depending on vehicle type and road test outcome. Performance on the Vitals tasks predicted on-road performance across all vehicle types. Participants who failed their on-road evaluation also demonstrated lower success on the Judgement task, fewer correctly replicated shapes on the Memory task, and less time on-target in the Control task compared to those who passed. Performance on cognitive and sensorimotor tasks is a good predictor of future driving performance and driver safety for commercially-licensed drivers. Regardless of vehicle type, stakeholders can use cognitive measures from the Vitals assessment to identify an increased driving risk. Use of the Vitals as a screening tool prior to on-road evaluation can benefit both drivers and evaluators.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".