How effective is the Trail Making Test (Parts A and B) in identifying cognitively impaired drivers?
Bibliographic record
Abstract
BACKGROUND: the medical community plays an important role in identifying drivers who may no longer be competent to drive due to illnesses such as dementia. Several office-based cognitive screening tools are currently used by the medical community, e.g. Mini-Mental State Examination, Trail Making Test (TMT), to assist in the identification of cognitively impaired (CI) at-risk drivers. However, the predictive validity of these tools is questionable. OBJECTIVE: to examine the predictive power of the TMT for on-road driving performance. METHODS: data from a prospective sample of CI and healthy older drivers were collected. TMT-A and -B (time and errors) served as predictor variables, with pass/fail on a scientifically based on-road assessment used as the criterion variable. Receiver operating characteristic (ROC) curve analysis was used to assess overall 'diagnostic' accuracy of TMT-A and -B for driving competency. Cut points from previous studies/guidelines were used to assess predictive power. FINDINGS: a total of 134 older drivers (mean age = 75.30; SD = 7.83) participated: 87 healthy controls and 47 CI individuals. All predictor variables, with the exception of TMT-A errors, were significantly correlated with driving outcome. However, results from ROC curve analyses indicated that only TMT-A and -B total time had moderate discriminative abilities. Results also indicate that the power of the TMT is the lowest where physicians need it most (e.g. identifying CI patients whose driving skills have declined to an unsafe level). CONCLUSION: TMT-A and -B outcomes are most likely to be inaccurate in those whose driving competency has declined to an unsafe level, resulting in risks to both individual and public safety.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".