Post-stroke visual neglect affects goal-directed locomotion in different perceptuo-cognitive conditions and on a wide visual spectrum
Bibliographic record
Abstract
BACKGROUND: Unilateral spatial neglect (USN), a highly prevalent and disabling post-stroke deficit, has been shown to affect the recovery of locomotion. However, our current understanding of USN role in goal-directed locomotion control, and this, in different cognitive/perceptual conditions tapping into daily life demands, is limited. OBJECTIVES: To examine goal-directed locomotion abilities in individuals with and without post-stroke USN vs. healthy controls. METHODS: Participants (n = 45, n = 15 per group) performed goal-directed locomotion trials to actual, remembered and shifting targets located 7 m away at 0° and 15° right/left while immersed in a 3-D virtual environment. RESULTS: Greater end-point mediolateral displacement and heading errors (end-point accuracy measures) were found for the actual and the remembered left and right targets among those with post-stroke USN compared to the two other groups (p < 0.05). A delayed onset of reorientation to the left and right shifting targets was also observed in USN+ participants vs. the other two groups (p < 0.05). Results on clinical near space USN assessment and walking speed explained only a third of the variance in goal-directed walking performance. CONCLUSION: Post-stroke USN was found to affect goal-directed locomotion in different perceptuo-cognitive conditions, both to contralesional and ipsilesional targets, demonstrating the presence of lateralized and non-lateralized deficits. Beyond neglect severity and walking capacity, other factors related to attention, executive functioning and higher-order visual perceptual abilities (e.g. optic flow perception) may account for the goal-directed walking deficits observed in post-stroke USN+. Goal-directed locomotion can be explored in the design of future VR-based evaluation and training tools for USN to improve the currently used conventional methods.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".