Impact of post-stroke unilateral spatial neglect on goal-directed arm movements: systematic literature review
Bibliographic record
Abstract
BACKGROUND: Unilateral spatial neglect (USN), a highly prevalent post-stroke impairment, refers to one's inability to orient or respond to stimuli located in the contralesional visual hemispace. Unilateral spatial neglect has been shown to strongly affect motor performance in functional activities, including non-affected upper extremity (UE) movements. To date, our understanding of the effects of USN on goal-directed UE movements is limited and comparing performance of individuals post-stroke with and without USN is required. OBJECTIVE: To determine, in individuals with stroke, how does the presence of USN, in comparison to the absence of USN, impacts different types of goal-directed movements of the non-affected UE. METHODS: The present review approach consisted of a comprehensive literature search, an assessment of the quality of the selected studies and qualitative data analysis. RESULTS: A total of 20 studies of moderate to high quality were selected. The USN-specific impairments were found in tasks that required a perceptual, memory-guided or delayed actions, and fewer impairments were found in tasks that required an immediate action to a predefined target. CONCLUSION: The results indicate that USN contributes to deficits observed in action execution with the non-effected UE that requires greater perceptual demands.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".