The Assessment of Driving Fitness Using an On-Road Evaluation in Patients With Cirrhosis
Bibliographic record
Abstract
INTRODUCTION: The association between cirrhosis and driving performance is of particular clinical relevance because of the life-threatening safety issues both for the driver with cirrhosis and the general public. Study aims were to assess (i) driving competency through the use of an in-office computerized battery and on-road driving assessment (DriveABLE) and (ii) the association between minimal hepatic encephalopathy (MHE), in-office paper-pencil tools, and additional measures (e.g., frailty, depression, cognitive testing) with unsafe driving. METHODS: Patients were prospectively recruited from 2 tertiary care liver clinics. In-office tests and in-office and on-road assessments of driving competence were completed. The χ 2 test and 1-way analysis of variance were used to analyze differences among those with and without MHE. Logistic regression was used to evaluate predictors of an indeterminate/fail result on the in-office computerized driving assessment battery (DriveABLE Cognitive Assessment Tool [DCAT]). RESULTS: Eighty patients participated with a mean age of 57 years, 70% male, 75% Child-Pugh B/C, and 36% with a history of overt hepatic encephalopathy. Thirty percent met MHE criteria on both the psychometric hepatic encephalopathy score and the Stroop app tests. Only 2 patients (3%) were categorized as "unfit to drive" in the on-road driving test, one with MHE and the other without. Fifty-eight percent of the patients were scored as indeterminate/fail on the DCAT. This corresponded to a higher mean number of on-road driving errors (5.3 [SD 2.1] vs 4.2 [SD 1.6] in those who passed the DCAT, P = 0.01). Older age (odds ratio 1.3; confidence interval 1.1, 1.5; P = 0.001) and MHE by Stroop/psychometric hepatic encephalopathy score (odds ratio 11.0; confidence interval 2.3, 51.8; P = 0.002) were independently predictive of worse performance on the DCAT. DISCUSSION: Worse performance in in-office testing was associated with worse scores on a computerized driving assessment battery and more on-road driving errors, but in-office tools were insufficient to predict on-road driving failures. A diagnosis of MHE should not be used alone to restrict driving in patients with cirrhosis. At-risk patients require on-road driving tests under the supervision of driving regulatory agencies. Future studies should continue to refine and evaluate in-office or at-home testing to predict driving performance.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".