Deficits in Attention and Visual Processing but not Global Cognition Predict Simulated Driving Errors in Drivers Diagnosed With Mild Alzheimer’s Disease
Bibliographic record
Abstract
This study sought to predict driving performance of drivers with Alzheimer's disease (AD) using measures of attention, visual processing, and global cognition. Simulated driving performance of individuals with mild AD (n = 20) was contrasted with performance of a group of healthy controls (n = 21). Performance on measures of global cognitive function and specific tests of attention and visual processing were examined in relation to simulated driving performance. Strong associations were observed between measures of attention, notably the Test of Everyday Attention (sustained attention; r = -.651, P = .002) and the Useful Field of View (r = .563, P = .010), and driving performance among drivers with mild AD. The Visual Object and Space Perception Test-object was significantly correlated with the occurrence of crashes (r = .652, P = .002). Tests of global cognition did not correlate with simulated driving outcomes. The results suggest that professionals exercise caution when extrapolating driving performance based on global cognitive indicators.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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".