Using Video Replay of Simulated Driving to Estimate Driving Safety and Cognitive Status
Bibliographic record
Abstract
Cognitive decline resulting from Dementia of Alzheimer’s Type (DAT) can lead to reduced ability to perform complex daily tasks required for independent living, including driving an automobile. This study explores the ability of untrained observers to classify driving safety using short video clips of simulated driving through intersections; it also examined whether untrained observers could predict whether the driver was cognitively healthy or cognitively impaired. Participants (n = 54) were shown a series of 30 video clips arranged in an online survey and asked to answer questions following each clip regarding the safety of the maneuver and the cognitive status of the driver. Results showed that participants’ subjectively rated DAT drivers as significantly less safe in comparison to control drivers, F (1, 52) = 228.44, p < 0.001. Participant’s classification of DAT drivers and controls was also significantly higher than chance (i.e., >50% correct). Findings provide preliminary support for the development of a clinical decision-making aid using video replay of driving simulator performance in fitness-to-drive assessments for individuals with cognitive impairment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".