P3‐281: A systematic literature review of the trail‐making test and its effectiveness in identifying cognitively impaired drivers
Bibliographic record
Abstract
The medical community is well-placed to identify cognitively impaired drivers who may no longer be safe to drive. However, the validity of commonly used screening tools for identification of cognitive impairment for decisions about driving is not well established. The primary objective of this research was to examine the accuracy of the Trail Making Test (Parts A and B) [TMT] for determination of driving competency. From an initial sample of 4,441 studies, 30 articles met the inclusionary/exclusionary criteria and were included in the systematic review (see Figure 1). The results from the 30 empirical studies yielded 46 unique data points, with 16 of those data points related to TMT-A (time or errors), 26 to TMT-B (time or errors), and 4 to TMT-B minus TMT-A (time). The majority (n = 36) of data points had on-road evaluation as the outcome measure, with 10 data points having crashes (n = 5) or simulator results (n = 5) as the outcome measure. The vast majority of studies relied on correlational analyses, with only 4 studies using cutpoints. Only one of the 4 studies using cutpoints examined sensitivity and specificity, with a reported sensitivity of 45% and a specificity of 86%. The results from the 30 empirical studies reveal that TMT-A and -B scores are associated with driving outcomes. However, knowledge of that association provides little in the way of information for decision making on driving competency in the clinical setting. Without the aid of empirically determined cut points, the use of the TMT for decision making on medical fitness to drive is questionable.
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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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".