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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".