A prospective study of cognitive tests to predict performance on a standardised road test in people with dementia
Bibliographic record
Abstract
OBJECTIVE: Previous work by Lincoln and colleagues produced a cognitive test battery for predicting safety to drive in people with dementia. The aim was to check the accuracy of this battery and assess whether it could be improved by shortening it, including additional cognitive tests, and a measure of previous driving. METHODS: Participants with dementia, who were driving, were recruited. They were assessed on cognitive tests including measures of concentration, executive function, visuospatial perception, verbal recognition memory, and speed of information processing. Patients were then assessed on the Nottingham Neurological Driving Assessment (NNDA) by an approved driving instructor (ADI), blind to cognitive test results. RESULTS: Seventy-five patients were recruited and completed the cognitive tests. Of these, 65 were assessed on the road. These participants were aged 59-88 (mean = 75.2, SD = 6.8) and 49 were men. Time driving varied from 19 to 73 years (mean = 52.5, SD = 10.0). Thirteen participants were unsafe and 52 safe to drive. Using a cut-off of > 0 to indicate safety to drive, the original predictive equations correctly classified 48 (76.2%) of 63 participants with complete data. Logistic regression including additional tests reduced misclassifications. CONCLUSIONS: A lower proportion of participants were found to be unsafe on the road than in previous studies. Nevertheless, the previously identified equation predicted safety to drive in most patients. Including additional tests reduced the misclassification rate but requires independent validation. We suggest that the cognitive test battery might be used in clinical practice to identify patients with dementia who would benefit from on-road assessment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".