Exploring the Predictive Ability of the Motor-Free Visual Perception Test (MVPT) and Trail Making Test (TMT) for On-Road Driving Performance
Bibliographic record
Abstract
IMPORTANCE: Resuming driving after a change in functional ability is challenging for patients with a neurological condition. Although a combination of assessment tools has been suggested for use in driving evaluation, resources and availability of tools have been a problem. OBJECTIVE: To examine the predictive ability of two commonly used tools, the Motor-Free Visual Perception Test (MVPT) and the Trail Making Test, Parts A and B (TMTA and TMTB), on on-road driving performance. DESIGN: Retrospective chart review of 82 patient charts between 2015 and 2016. SETTING: Local rehabilitation hospital. PARTICIPANTS: Eighty-two patients with a primary neurological diagnosis (general neurological condition, n = 13; spinal cord injury, n = 11; stroke, n = 58). OUTCOMES AND MEASURES: MVPT, TMTA, and TMTB. RESULTS: Among the patients, 36 passed and 46 failed the on-road evaluation. The TMTA and TMTB scores were significantly different between those who passed or failed the on-road evaluation. Logistic regression analyses revealed that the TMTB completion time was the only significant predictor of on-road driving performance (for the all-patient model, 66% prediction accuracy, -2 log-likelihood [LL] = 93.47, exp β = 0.98; for the stroke-only model, 76% prediction accuracy, -2LL = 59.61, exp β = 0.97). CONCLUSIONS AND RELEVANCE: Our findings suggest that the TMTB is a better predictor of on-road driving performance for patients with a neurological condition than the MVPT. The findings shed light on the importance of selecting proper tools when assessing driving performance. Future prospective studies with a wider array of predictive variables are recommended to support the present findings. WHAT THIS ARTICLE ADDS: Occupational therapists should revisit the use of the MVPT in driving assessment and consider multiple assessment tools when evaluating and predicting driving performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".