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 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 | 0.001 | 0.006 |
| 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 | 0.000 |
| 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".