Questioning the Impact of Vestibular Rehabilitation in Mal de Debarquement Syndrome
Bibliographic record
Abstract
INTRODUCTION: Mal de debarquement syndrome (MdDS) is a rare and poorly understood clinical entity defined as a persistent sensation of rocking and swaying that can severely affect the quality of life. To date, the treatment options are very limited. Even though vestibular rehabilitation (VR) efficacy following peripheral vestibular lesion is well-documented, little is known about its influence on MdDS. The objective of the study was to explore the influence of traditional VR program on postural control in a patient diagnosed with MdDS. METHODS: We assessed 3 different participants: 1 healthy control; 1 participant with identified peripheral vestibular impairment (VI); 1 participant diagnosed with MdDS. Postural control was assessed using a force plate (AMTI, Accusway). Participants were assessed following the modified Clinical Test Sensory Integration Balance protocol (mCTSIB, eyes open on firm surface/eyes closed on firm surface/eyes open on foam/eyes closed on foam). The raw data were exported and analyzed in a custom-made Matlab script (Matlab R2020a). We retrieved the center of pressure velocity in both anterior-posterior and mediolateral directions and performed an analysis of the frequency content using Daubechies wavelet of order 4 with 6 levels of decomposition. Protocol VI and MdDS patients performed a 4-week VR program. Postural control, using a force plate, and Dizziness Handicap Inventory (DHI) were assessed before and after the VR program. Healthy control was assessed twice separated by 1 week without any specific intervention. RESULTS: VI participant showed clear improvement on DHI and sway velocity on condition eyes closed with foam. Accordingly, a reduction of energy content within frequency bands (0.39-0.78 Hz and 0.78-1.56 Hz) was observed post-rehabilitation for VI participant in both conditions with foam. Interestingly, MdDS participant demonstrated a reduction in sway velocity in most of the conditions but the frequency content was not modified by VR and was comparable to healthy control. Accordingly, the DHI of the MdDS participant failed to demonstrate any difference following VR. CONCLUSION: The results of the present study question the use of VR as an efficient treatment option for MdDS. Future studies must recruit a larger sample size and focus on the relationship between illusion of movement and postural characteristics such as sway velocity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".