Effect of Multi-site Variabilities on Electrovestibulography: Environmental and Physical Factors
Bibliographic record
Abstract
Background: There are physiological changes in pathologies such as Alzheimer’s Disease (AD) within the lower vestibuloacoustic system, which may be potentially useful when used as neurodegeneration features. We hypothesize two Electrovestibulography feature types (Field Potential (FP) shape and the Firing pattern of detected FP’s) may have utility as Neurodegeneration features. Our long term objective is to use a population of Parkinson’s Disease (PD), AD, Post-Concussion Syndrome (PCS), Bipolar Disorder (BD) and Major Depressive Disorder (MDD) patients together with individual pathology-wise age and gender matched control cohorts to determine the degree to which each of these pathologies varies from controls and in proportion to the level of Neurodegeneration often associated (either temporarily or permanently) with each pathology. However, before such a comparison can be made it is necessary to ensure the various populations recorded across different countries are comparable. This paper determines which populations are comparable. Methods: An initial comparison of AD (with N = 16) and a best matched healthy control population (with specific age/gender/recording site/electrode matched controls) from a pool of 112 controls produced two EVestG features (FP shape and FP firing pattern). These features were examined for their variability with respect to electrode type, age, gender, powerline frequency and environmental factors. Results: Age and gender did not have a significant impact on the features. Powerline and environmental artefacts could be accounted for by filtering; thus, they did not significantly affect the features measured. However, electrode type had a significant effect on the extracted features. Conclusions: For the two EVestG features tested only electrode type had a significant effect on the recordings, and hence the extracted features. Thus, only populations with the same electrode type can be compared.
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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 | 0.007 |
| 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) | 0.003 | 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".