Virtual reality treatment and assessments for post-stroke unilateral spatial neglect: A systematic literature review
Bibliographic record
Abstract
Unilateral spatial neglect (USN) is a highly prevalent post-stroke deficit. Currently, there is no gold standard USN assessment which encompasses the heterogeneity of this disorder and that is sensitive to detect mild deficits. Similarly, there is a limited number of high quality studies suggesting that conventional USN treatments are effective in improving functional outcomes and reducing disability. Virtual reality (VR) provides enhanced methods for USN assessment and treatment. To establish best-practice recommendations with respect to its use, it is necessary to appraise the existing evidence. This systematic review aimed to identify and appraise existing VR-based USN assessments; and to determine whether VR is more effective than conventional therapy. Assessment tools were critically appraised using standard criteria. The methodological quality of the treatment trials was rated by two authors. The level of evidence according to stage of recovery was determined. Findings were compiled into a VR-based USN Assessment and Treatment Toolkit (VR-ATT). Twenty-three studies were identified. The proposed VR tools augmented the conventional assessment strategies. However, most studies lacked analysis of psychometric properties. There is limited evidence that VR is more effective than conventional therapy in improving USN symptoms in patients with stroke. It was concluded that VR-ATT could facilitate identification and decision-making as to the appropriateness of VR-based USN assessments and treatments across the continuum of stroke care, but more evidence is required on treatment effectiveness.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".