Clinical predictors of driving simulator performance in drivers with multiple sclerosis
Bibliographic record
Abstract
Background: Drivers with multiple sclerosis (MS) may experience visual–cognitive impairment that affects their fitness to drive. Due to limitations associated with the on-road assessment, an alternative assessment that measures driving performance is warranted. Whether clinical indicators of on-road outcomes can also predict driving performance outcomes on a driving simulator are not fully understood. Objective: This study examined if deficits in immediate verbal/auditory recall (California Verbal Learning Test–Second Edition; CVLT2-IR) and/or slower divided attention (Useful Field of View™; UFOV2) predicted deficits in operational, tactical, or strategic maneuvers assessed on a driving simulator, in drivers with and without MS. Methods: Participants completed the CVLT2-IR, UFOV2, and a driving simulator assessment of operational, tactical, and strategic maneuvers. Results: Deficits in immediate verbal/auditory recall and slower divided attention predicted adjustment to stimuli errors, pertaining to tactical maneuvers only, in 36 drivers with MS (vs 20 drivers without MS; F(3, 51) = 6.1, p = 0.001, R 2 = 0.3, [Formula: see text]). Conclusion: The CVLT2-IR and UFOV2 capture the visual and verbal/auditory recall, processing speed, and divided attention required to appropriately adjust to stimuli in a simulated driving environment. Clinicians may use the CVLT2-IR and UFOV2 as precursors to driving performance deficits in drivers with MS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".