Assessment Tools for Evaluating Fitness to Drive: A Critical Appraisal of Evidence
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: Many office-based assessment tools are used by occupational therapists to predict fitness to drive. PURPOSE: To appraise psychometric properties of such tools, specifically predictive validity for on-road performance. METHODS: A literature search was conducted to identify assessment tools and studies involving on-road outcomes (behind-the-wheel evaluation, crashes, traffic violations). Using a standardized appraisal process, reviewers rated each tool's psychometric properties, including its predictive validity with on-road performance. FINDINGS: Seventeen measures met the inclusion criteria. Evidence suggests many tools do not have cutoff scores linked with on-road outcomes, although some had stronger evidence than others. Implications. When making a determination regarding driver fitness, clinicians should consider the psychometric properties of the tool as well as existing evidence concerning its utility in predicting on-road performance. Caution is warranted in using any one office-based tool to predict driving fitness; rather, a multifactorial-based assessment approach that includes physical, cognitive, and visual-perceptual components, is recommended.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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