Assessment of Drivers with Alzheimer’s Disease in High Demand Driving Situations: Coping with Intersections in a Driving Simulator
Bibliographic record
Abstract
Intersections are one of the most complex and cognitively demanding driving situations. Individuals with dementia and, more precisely, Alzheimer's disease (AD), may face additional challenges negotiating intersections given the nature of their cognitive decline, which often includes deficits of attention. We developed a comprehensive evaluation scheme to assess simulated driving performance at intersections. The evaluation scheme captured all types of errors that could occur during preparation (i.e., prior to the intersection), execution (i.e., during the intersection), and recovery (i.e., after the intersection). Using the evaluation scheme, intersection behaviour in a driving simulator among 17 drivers with mild AD was compared to that of 21 healthy controls. The results indicated that across all types of intersections, mild AD drivers exhibited a greater number of errors relative to controls. Drivers with mild AD made the most errors during the preparation period leading up to the intersection. These findings present a novel approach to analyzing intersection behaviour and contribute to the growing body of research on dementia and driving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".