How have neck muscle vibration effects on visuospatial behavior and spatial neglect been explored? A scoping review
Bibliographic record
Abstract
Introduction Neck muscle vibration (NMV) has been proposed as a bottom-up intervention to enhance visuospatial exploration in post-stroke patients with unilateral spatial neglect (USN). While some studies report enlarged visual exploration during NMV application, others find no significant impact, highlighting inconsistencies in the literature. The diversity in NMV application methods and the variation in visuospatial outcome measures may contribute to these conflicting findings. This study aimed to overview the methodological approaches used to investigate NMV’s effects on visuospatial behavior in USN patients, focusing on aspects beyond sample size and study design.Methods Among the seven databases, studies that applied NMV and assessed visual or perceptual outcomes were included in the analysis. Two independent reviewers screened titles and abstracts to select the studies to analyze. Data about the publication, population, modalities of application, and outcomes were extracted and synthesized.Results Twenty-five publications from 1988 to 2023 were included, involving a total of 512 participants, of whom 114 (22.3%) had USN. The presence of USN was assessed based on paper-and-pencil tests. The frequency of NMV was set between 80 and 125 hz. In 22 studies, NMV were applied on the left side of the neck muscles. Six studies proposed multiple NMV sessions, lasting between 5 and 50 minutes/day, 3–5 times/week, for 2–4 weeks. One study included a follow-up period of up to 1.4 years. The tasks during NMV often involved indicating the subjective straight ahead (SSA, n = 8), pointing out targets, or no specific activity (n = 7, each). The SSA and cancellation tests were the most frequent outcomes.Conclusions NMV application modalities varied widely across studies, with only vibration frequency showing consistency. The tasks performed during NMV and the outcome measures were diverse and generally unrelated to activities of daily living. Therefore, NMV effects during more ecologically valid tasks should be explored.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".