A Pilot Study to Assess Visual Vertigo in People with Persistent Postural–Perceptual Dizziness with a New Computer-Based Tool
Bibliographic record
Abstract
Background: Visual vertigo (VV) is a common symptom in people with persistent postural–perceptual dizziness (PPPD). Few subjective scales are validated for assessing the intensity of VV, yet these scales are limited by recall bias, as they require individuals to rate their symptoms from memory. The computer-Visual Vertigo Analogue Scale (c-VVAS) was developed by adapting five scenarios from the original paper-VVAS (p-VVAS) into 30 s video clips. The aim of this pilot study was to develop and test a computerized video-based tool for the assessment of visual vertigo in people with PPPD. Methods: PPPD participants (n = 8) and age- and sex-matched controls (n = 8) completed the traditional p-VVAS and the c-VVAS. A questionnaire about their experiences using the c-VVAS was completed by all participants. Results: There was a significant difference between the c-VVAS scores from the PPPD and the control group (Mann–Whitney, p < 0.05). The correlation between the total c-VVAS scores and the total c-VVAS scores was not significant (r = 0.668, p = 0.07). The study showed a high acceptance rate of the c-VVAS by participants (mean = 91.74%). Conclusion: This pilot study found that the c-VVAS can distinguish PPPD subjects from healthy controls and that it was well-received by all participants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".