A Systematic Review and Meta-Analysis on the Association Between Driving Ability and Neuropsychological Test Performances after Moderate to Severe Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVES: 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 offer an estimate of driving ability. To examine this, a meta-analysis of the relationship between neuropsychological testing and driving ability after TBI was performed. METHODS: Hedge's g and 95% confidence intervals were calculated using a random effects model. Analyses were performed on cognitive domains and individual tests. Meta-regressions examined the influence of study design, demographic, and clinical factors on effect sizes. RESULTS: Eleven studies were included in the meta-analysis. Executive functions had the largest effect size (g = 0.60 [0.39-0.80]), followed by verbal memory (g = 0.49 [0.27-0.71]), processing speed/attention (g = 0.48 [0.29-0.67]), and visual memory (g = 0.43 [0.14-0.71]). Of the individual tests, Useful Field of Vision (UFOV) divided attention (g = 1.12 [0.52-1.72]), Trail Making Test B (g = 0.75 [0.42-1.08]), and UFOV selective attention (g = 0.67 [0.22-1.12]) had the largest effects. The effect sizes for Choice Reaction Time test and Trail Making Test A were g = 0.63 (0.09-1.16) and g = 0.58 (0.10-1.06), respectively. Years post injury (β = 0.11 [0.02-0.21] and age (β = 0.05 [0.009-0.09]) emerged as significant predictors of effect sizes (both p < .05). CONCLUSIONS: These results provide preliminary evidence of associations between neuropsychological test performance and driving ability after moderate to severe TBI and highlight moderating effects of demographic and clinical factors.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".