Screening Tools for Cognitive Function and Driving [Internet]
Bibliographic record
Abstract
There are various reasons why persons holding a driver’s license no longer retain the ability to drive a car. This might be e.g. stroke, traumatic brain damage, or early dementia. In order to assess the driving ability in persons with suspected cognitive impairment, there is a need for good tests that can categorize persons into three groups: (1) inability to drive a car, (2) sufficient ability to drive a car, (3) should be referred to a more comprehensive assessment of cognitive ability.In this report, we have provided an overview of existing cognitive screening tests for assessing functions of relevance for ability to drive a car, and how good the tests are for predicting who will pass an on-road driving test or who will experience a car accident during the first years after the screening test.Our key messages are: We have not found any cognitive screening tests that have good documentation of diagnostic test accuracy for predicting results on on-road driving tests. Tests that could detect at least 65 percent of dangerous drivers in all studies were the Montreal Cognitive Assessment (MoCa, detected 70-85%), the Clock Drawing Test (detected 65-71%) and the Trail-Making Test-B (detected 70-77%). We have in most cases little or very little confidence in the results There was large variation in how good the tests were for predicting results on an on-road test There is a need for standardization of the outcome measures and the test batteries in research about screening tests for driving ability We can therefore not conclude about which tests are best for detecting persons with a reduced ability to drive among persons with a suspected cognitive impairment
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".