Can prism adaptation effects generalize to wheelchair maneuvering?
Bibliographic record
Abstract
BACKGROUND: While prism adaptation (PA) has been recognized as a promising tool for treating spatial neglect, implementation as a standard treatment in clinical care has been lagging. Limited evidence for the generalization of after-effects to everyday activities has been a barrier towards implementation. OBJECTIVES: This study examined whether a home-friendly standardized PA protocol (Peg-the-Mole, PTM) induces after-effects that can transfer to wheelchair maneuvering. We also examined the impact of using constant (1 starting hand position) or variable (3 starting hand positions) training conditions on the transfer of after-effects to wheelchair maneuvering. METHODS: Sixty participants were randomly assigned to one of four PTM conditions: 1) prisms/constant training; 2) prisms/variable training; 3) sham goggles/constant training; 4) sham goggles/variable training. RESULTS: The use of PTM with rightward shifting prisms induced after-effects on proprioceptive and visual pointing outcome tasks. Groups using PTM with prism goggles showed a leftward shift in their position within a wheelchair course and a reduction in the number of right-sided collisions. The training condition did not have an impact on the transfer of after-effects to wheelchair driving. CONCLUSION: PTM is a clinically appealing PA protocol that induces after-effects that can transfer to an everyday activity relevant to patients with neglect.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".