Assessing executive function in relation to fitness to drive: A review of tools and their ability to predict safe driving
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/AIM: The assessment of executive functions is an integral component in determining fitness to drive. A structured review was conducted to identify assessment tools used to measure executive function in relation to driving and to describe these tools according to: (i) specific executive function components assessed; (ii) the tool's validity in predicting safe driving; and (iii) clinical utility. METHODS: Sixty-nine articles were reviewed, identifying 53 executive function tools/assessments used in driving research. Each tool was critically appraised and the findings were compiled in a Driving Executive Function Tool Guide. RESULTS: Among the 53 tools, there were 27 general assessments of cognition, 19 driving-specific and seven activities of daily living/instrumental activities of daily living assessments. No single tool measured all executive function components: working memory was the most common (n = 20/53). Several tools demonstrated strong predictive validity and clinical utility. For example, tools, such as the Trail Making Test and the Maze Task, have the shortest administration time (i.e. often less than 10 minutes) and the most easily accessible method of administration (i.e. pen and paper or verbal). Driving-specific tools range from short questionnaires, such as the 10-minute Manchester Driving Behaviour Questionnaire, to more complex tools requiring about 45 minutes to administer. CONCLUSIONS AND SIGNIFICANCE OF THE STUDY: The appropriateness of a tool depends on the individual being assessed and on practical constraints of the clinical context. The Driving Executive Function Tool Guide provides useful information that should facilitate decision-making and selection of appropriate executive function tools in relation to driving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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