Assessment of Driving‐Related Skills Prediction of Unsafe Driving in Older Adults in the Office Setting
Bibliographic record
Abstract
OBJECTIVES: To examine the sensitivity and specificity of the Assessment of Driving-Related Skills (ADReS), a clinical tool recommended by the American Medical Association for identifying potentially unsafe older drivers that includes tests of vision, motor function, and cognition. DESIGN: Cross-sectional observation study. SETTING: Memory assessment outpatient clinic of a university hospital. PARTICIPANTS: Drivers with normal cognition (n = 47) and cognitive impairment (n = 75). MEASUREMENTS: A neurologist completed the ADReS during an office visit. Additional cognitive tests of executive, visuospatial, and visuomotor function were also performed. On a separate day, participants completed a standardized on-road test, assessed by a professional driving instructor using a global safety rating and a quantitative driving score. RESULTS: In this sample of currently active older drivers with and without cognitive impairment, measures of cognition-particularly the Trail-Making Test Part B-were more highly correlated with driving scores than other measures of function. Using recommended scoring procedures, the ADReS had a sensitivity of 0.81 for detecting impaired driving on the road test, with a specificity of 0.32 and an area under the receiver operating characteristic curve (AUC) of 0.57. A logistic regression model that incorporated computerized maze task and Mini-Mental State Examination scores improved overall classification accuracy, yielding a sensitivity of 0.61, a specificity of 0.84, and an AUC of 0.80. CONCLUSION: In its present form, the ADReS has limited utility as an office screen for individuals who should undergo formal driving assessment. Improved scoring methods and screening tests with greater diagnostic accuracy than the ADReS are needed for general office practice.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".