Driving Errors That Predict On-Road Outcomes in Adults With Multiple Sclerosis
Bibliographic record
Abstract
Driving errors that predict on-road outcomes for persons with multiple sclerosis (PwMS) are not well studied. The objective of this study was to determine whether adjustment-to-stimuli and gap acceptance errors significantly predict passing/failing a standardized on-road assessment of PwMS. Thirty-seven participants completed visual ability and visual attention assessments, and participated in an on-road assessment, where seven types of driving errors and pass/fail outcomes were determined. Adjustment-to-stimuli (No.) and gap acceptance errors (commit/did not commit) significantly predicted passing/failing the on-road assessment, with an area under the curve of 91.6% ( p < .0001). With no gap acceptance errors committed, five adjustment-to-stimuli errors optimally determined pass/fail outcomes in PwMS. Furthermore, with no adjustment to stimuli errors committed, committing any gap acceptance errors also optimally determined pass/fail outcomes in PwMS. Further research may focus on visual, cognitive, and/or motor impairments underlying adjustment-to-stimuli and gap acceptance errors for eventual development of rehabilitation strategies for PwMS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".