Neuropsychological and Fatigue Predictors of Driving Ability After Mild to Moderate Traumatic Brain Injury: A Meta-analysis and Pilot Study
Bibliographic record
Abstract
Background. Guidelines on return to driving after traumatic brain injury (TBI) are scarce. Since driving requires the coordination of multiple cognitive, perceptual, and psychomotor functions, neuropsychological testing may estimate driving ability. Further, the impact of somatic symptoms of TBI, including mental and physical fatigue, have rarely been considered on the relationship between neuropsychological test and driving performance. Two studies examined these issues: a meta-analysis to quantitatively review the literature on the relationship between neuropsychological testing and driving ability after TBI (Study 1), and a pilot study to explore the neuropsychological measures that may predict safe driving–based on the results of the meta-analysis–and extended the literature by examining how clinical and demographic factors affect the cognitive and driving relationship (Study 2). Methods. In Study 1, Hedge’s g and 95% confidence intervals were calculated using a random effects model. Analyses were performed on neuropsychological domains and individual tests. Metaregressions examined the influence of study design, demographic, and clinical factors on effect sizes. In Study 2, patients with TBI and healthy controls completed a neuropsychological test battery, questionnaires on clinical status, and a driving simulation. The correlations between neuropsychological test and driving performance was examined, and the effect of somatic symptoms on this relationship was examined using partial correlations. Results. Eleven studies were included in the meta-analysis of Study 1. Measures of executive functions had the largest effect size, followed by verbal memory, processing speed/attention, and visual memory; these patterns emerged both in analyses of neuropsychological domains and individual tests. Years post injury and age emerged as significant predictors of effect sizes. Study 2 confirmed many of the findings of the meta-analysis, and measures of executive functions, processing speed, verbal and visual memory, and attention were correlated with driving outcomes. Fatigue, sleep disturbance, and pain severity impacted the neuropsychological test and driving performance relationship. Conclusions. These studies provide initial evidence for the relationship between neuropsychological test and driving performance, and the effect of somatic symptoms in patients with TBI. The data provide impetus for future research examining the clinical application and predictive value of individual neuropsychological tests in driving assessments.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.039 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".