Systematic review of the evidence for Trails B cut-off scores in assessing fitness-to-drive
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Fitness-to-drive guidelines recommend employing the Trail Making B Test (a.k.a. Trails B), but do not provide guidance regarding cut-off scores. There is ongoing debate regarding the optimal cut-off score on the Trails B test. The objective of this study was to address this controversy by systematically reviewing the evidence for specific Trails B cut-off scores (e.g., cut-offs in both time to completion and number of errors) with respect to fitness-to-drive. METHODS: Systematic review of all prospective cohort, retrospective cohort, case-control, correlation, and cross-sectional studies reporting the ability of the Trails B to predict driving safety that were published in English-language, peer-reviewed journals. RESULTS: Forty-seven articles were reviewed. None of the articles justified sample sizes via formal calculations. Cut-off scores reported based on research include: 90 seconds, 133 seconds, 147 seconds, 180 seconds, and < 3 errors. CONCLUSIONS: There is support for the previously published Trails B cut-offs of 3 minutes or 3 errors (the '3 or 3 rule'). Major methodological limitations of this body of research were uncovered including (1) lack of justification of sample size leaving studies open to Type II error (i.e., false negative findings), and (2) excessive focus on associations rather than clinically useful cut-off scores.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it