Feasibility of utilizing clinical and driving simulator assessments to indicate driving performance deficits in adults with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Drivers with Multiple Sclerosis (MS) experience visual-cognitive impairment that impact on-road driving performance. OBJECTIVE: This study examines the feasibility of utilizing visual-cognitive and driving simulator assessments to indicate driving performance deficits (operational, tactical, and strategic maneuvers) in drivers with MS. METHODS: Through an evidence-informed feasibility framework, we evaluated recruitment capability and resulting sample characteristics, data collection procedures and outcome measures, participants’ acceptability and suitability of the driving simulator, the resources and ability to implement the study, and clinical and driving simulator assessment results. RESULTS: Thirty-eight persons with MS (median age [Formula: see text] 43 years, IQR [Formula: see text] 19) and 21 persons without MS (median age [Formula: see text] 41 years, IQR [Formula: see text] 14) participated. Missing data on the driving simulator resulted from scenario complexity (13 with MS, 4 without MS) or the onset of simulator sickness (1 with MS, 1 without MS). Seven participants with MS and two participants without MS reported symptoms of simulator sickness. Participants with MS (vs without MS) made more adjustment to stimuli errors (tactical maneuvers). For participants with MS, immediate verbal/auditory recall or divided/selective attention correlated with simulated driving maneuvers. CONCLUSIONS: Study findings identified challenges (missing data, simulator sickness), but established feasibility for executing a full-scale study to predict driving simulator performance in drivers with MS.
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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 | 0.020 | 0.042 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".