Self-Evaluation of Driving Simulator Performance After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Despite the potential dangers associated with premature return to driving after stroke, very little research has examined the relationship between impaired self-awareness (ISA) and driving. This study examined self-awareness of driving simulator and neuropsychological performance among stroke patients, comparing them with healthy control participants. METHODS: Thirty stroke survivors and 30 controls each were asked for prediction and postdiction ratings of their performance on various driving simulator and neuropsychological tasks. Self-estimates versus actual performance discrepancy scores were calculated for various simulator and neuropsychological measures by converting scores to a shared metric. RESULTS: Across all measures, the stroke survivors greatly overestimated their performance in comparison with the accuracy of self-evaluations among the controls, thus suggesting ISA. This pattern of overestimating was observed on both novel (neuropsychological) and familiar (driving) tasks. However, there was some evidence to suggest that stroke survivors can benefit from feedback, as seen by increased accuracy in postdiction versus prediction self-evaluation scores. Both stroke survivors and controls also showed a greater shift toward accurate self-estimation on postdiction of driving performance than on postdiction of neuropsychological test performance. CONCLUSION: Although the temporal stability of the shift in awareness is not known, these results support the use of driving simulators as a useful and safe method of assessing and potentially improving stroke survivors' ISA.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".