Does prism adaptation treatment reduce spatial neglect and improve function?
Bibliographic record
Abstract
The potential of using prism adaptation for treating spatial neglect (SN) was questioned when recent meta-analyses found inconsistent evidence. However, analyses of clinical datasets support the use of prism adaptation treatment (PAT) in reducing SN and improving function. The main objective of this review is to evaluate the current state of the evidence of PAT therapeutic effects, identify knowledge gaps, and make suggestions to guide further research and support clinical decision-making. We used the framework of the National Institutes of Health (NIH) Stage Model for Behavioral Intervention Development which provides guidance on best practices for developing effective behavioral interventions that can be implemented in real-world settings. This model emphasizes the interplay between mechanisms underlying therapeutic effects ("who" should receive the treatment and "how" best does it work?) and considerations of adaptability and feasibility in real-world settings. The present critical review led to the following conclusion: the use of the NIH Stage Model reveals the heterogeneity of PAT studies and challenges in advancing PAT as an effective intervention. The key mechanisms such as prism strength, treatment intensity, arm visibility and activities during treatment, and evaluation methods lack consensus. Therefore, clinical research teams must continue to collect evidence to determine critical mechanisms and the optimal protocol. Further research identifying the optimal PAT protocol is needed before another meta-analysis on PAT's clinical efficacy should be conducted again.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".