Spectral Composition of Body Sway in Persistent Postural-Perceptual Dizziness
Bibliographic record
Abstract
OBJECTIVE: Previous studies in phobic postural vertigo patients showed characteristic frequency changes in body sway fluctuations, raising the question whether similar spectral changes can be also observed in the recently defined syndrome of persistent postural-perceptual dizziness (PPPD). STUDY DESIGN: Cross-sectional prospective study. SETTING: Tertiary referral center. SUBJECTS: Sixty-one PPPD patients and 41 healthy controls. INTERVENTIONS: Static balance was assessed while standing on firm surface with eyes open or closed (conditions 1 and 2) and while standing on foam with eyes open or closed (conditions 3 and 4). Postural sway was analyzed by means of time (sway area and standard deviation) and frequency domain metrics. The latter was based on comparisons of the percentage of energy in each of three frequency bands: low (0-0.5 Hz), middle (0.05-2 Hz), and high frequency (2-20 Hz). MAIN OUTCOME MEASURE: Stabilometric time and frequency domain parameters. RESULTS: Time domain metrics deteriorated significantly from conditions 1 through condition 4 in patients and controls. Spectral changes, however, were more abundant in PPPD subjects than in controls. Patients showed increased low frequency, but decreased high frequency spectral power in condition 3 as compared to condition 2. Dizziness Handicap Inventory score was positively correlated with middle frequency and negatively correlated with low frequency fluctuations. CONCLUSIONS: We conclude that PPPD patients exhibit a time domain sway pattern in different conditions which is grossly similar to that of controls. However, sensory feedback conditions with equal sway area show unique differences in their spectral content in PPPD patients. Moreover, perceived severity of dizziness is associated with greater body oscillations in the middle frequency band.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".