Validity and Responsiveness of the Visual Vertigo Analogue Scale
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: People with visual vertigo have dizziness provoked by visual stimulation. We have developed a Visual Vertigo Analogue Scale (VVAS) to evaluate their symptoms and response to rehabilitation. Our goal was to validate the VVAS against the Situation Characteristics Questionnaire (SITQ) score and determine its responsiveness to treatment. METHODS: Participants (n = 115) completed 3 questionnaires: Dizziness Handicap Inventory (DHI), VVAS, and the SITQ at their initial and final sessions of vestibular rehabilitation. The SITQ was analyzed using the Space Motion Discomfort (SMD1) outcome measure and by calculating the average score of all the items (SMDavg). The results were stratified into those who had a significant clinical change in their DHI score posttreatment and those who did not. Associations of the VVAS with SMD scores and change scores were determined by Pearson and Spearman correlations. Nonparametric t tests were used to compare the 2 DHI groups on the clinical outcomes. RESULTS: There were significant associations between VVAS scores (P < 0.0001) and both SMD1 (βVVAS = 0.02) and SMDavg scores (βVVAS = 0.03). Significant differences existed between the 2 DHI groups for all scores: VVAS (P = 0.0002), SMD1 (P = 0.02), and SMDavg (P = 0.0001). DISCUSSION AND CONCLUSIONS: VVAS scores correlated well with SMD1 and SMDavg scores. Changes in VVAS pre- and posttreatment scores corresponded to the changes seen in SMD1 and SMDavg scores. The VVAS shows validity and responsiveness to change. The VVAS can be used to detect clients with visual vertigo and to verify the progression of the client's symptoms.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A258).
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".