Exploring the effects of virtual reality on unilateral neglect caused by stroke: Four case studies
Bibliographic record
Abstract
Unilateral neglect is a common deficit following stroke which may impactfunction, safety and visual awareness. Virtual reality (VR) may be a potential new intervention option. Using a single-subject, A 1 -B-A 2 design, four case studies explored the effects of VR on unilateral neglect. During the intervention phase (B), participants completed six, weekly, one-hour sessions of VR tasks. The outcome measures completed during the baseline (A 1 ), treatment (B) and reassessment phase (A 2 ) were the Behavioural Inattention Test, and the Bells test. The data were graphically represented and subsequently visually analyzed. Qualitative comments from the participants are considered along with the results. The quantitative results are inconclusive but suggest that VR may have the potential to be useful for individuals with stroke who are affected by neglect in their everyday life. Further research is warranted to substantiate these results.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".