2201 – The Diagnosis Of Neurological Conditions Using Electrovestibulography (evestg)
Bibliographic record
Abstract
Introduction Dizziness is a defining condition of many pathologies within DSM4. There are many emotional and behavioural impacts on the balance system. EVestG is a purported test of the balance system that has been applied to the detection of schizophrenia. However, there is a need to show whether the EVestG recordings indeed contain vestibular signals. Objective To investigate a clear vestibular response in EVestG recordings by analysing the signals in response to whole body passive tilts. Methods EVestG signals were recorded in the ear canals of 5 healthy controls (50-69yrs) and 3 unmedicated Schizophrenics (29-53yrs) in response to whole body tilts. The signals were bandpass filtered (700-4000Hz) to remove muscle interference. The Root Mean Square (RMS) of the filtered signals was measured across 0.5 sec running windows (one sample at a time) and compared between the background and tilting responses. Results A typical example of the RMS signal for control subjects is shown in Fig 1. During the movement phase t=20-23 and 40-43 sec wherein the vestibular is active the RMS signal showed a marked increase for all signals of all subjects. The typical Schizophrenic response had the peaks seen at t=20-23 skewed to the left. Fig. 1 [figure 1] Conclusions EVestG signals do show a vestibular component. When validated with larger sample size may be assistive in neurological disorder diagnosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".