Ability of the Maze Navigation Test, Montreal Cognitive Assessment, and Trail Making Tests A & B to predict on-road driving performance in current drivers diagnosed with dementia.
Bibliographic record
Abstract
AIM: This study aimed to evaluate the ability of the Maze Navigation Test (MNT), Montreal Cognitive Assessment (MoCA) and Trail Making Tests A & B (TMT A & B) to predict on-road driving performance in current drivers diagnosed with dementia. METHODS: Current drivers with a diagnosis of dementia in whom there were clinical concerns about their driving safety were invited to participate between December 2014 and February 2018. Participants completed the MNT, MoCA and TMT A & B, then underwent a blinded specialist Occupational Therapy & Rehabilitation Service (OTRS) off-road and on-road driving assessment. RESULTS: Of the 34 participants, 19 (55.9%) retained their full license and 15 (44.1%) received driving restrictions (including cessation). Only completion time for the MNT (AUC .737, p=.019), the MoCA domain of attention (AUC .809, p=.003) and a combination of the MoCA domain of attention and visuospatial/executive (AUC .783, p=.006) predicted outcome. Derived optimal cut-scores were <443s for MNT completion time (sensitivity 73.3%, specificity 68.4%), <5/6 for MoCA-attention (sensitivity 73.3%, specificity 72.2%) and <8/11 for MoCA-visuospatial/executive+attention (sensitivity 80%, specificity 66.7%). Using these derived cut-scores, MNT completion time predicted poor performance during the on-road assessment in the domains of speed control (p=.039), planning/judgement (p=.004) and vehicle position (p=.028). CONCLUSION: Results of this study indicate MNT completion time and the MoCA domains of attention and visuospatial/executive could be used to inform driving ability and further referral for a specialist driving assessment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".