The evolution of Electrovestibulography technique and safety considerations
Bibliographic record
Abstract
Over the past decade, the number of papers reporting the use of the Electrovestibulography (EVestG) technique has tripled compared to the previous decade. Moreover, EVestG has been employed in clinical trials for diagnostic purposes and monitoring treatment efficacy. The key drivers behind the expansion of such work could be linked to both the progress achieved in the EVestG technical development as well as the fact that EVestG has proved to be a safe and tolerable technology with promising diagnostic capabilities. Compared to existing vestibular and neurophysiological assessments, EVestG provides a non-invasive and objective method to directly measure vestibular responses and indirectly assess neurophysiological brain activity, with potential for early diagnosis. This contribution reviews the technical evolution and safety considerations of EVestG over the last decade. Areas of development that together contributed to the current state of the art are discussed. These include the design of low-noise electrodes, the electrode placement protocol, and improvements in signal acquisition during recording. Additionally, participant attrition rates and withdrawal reasons are presented. Findings highlight advancements in signal quality, user comfort, and diagnostic reliability, reinforcing EVestG's clinical viability. Lastly, potential developments and challenges toward a miniaturised and portable EVestG technology are discussed.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".