Investigating the feasibility of EVestG assessment for screening concussion
Bibliographic record
Abstract
Electrovestibulography (EVestG™) is a new technology that objectively measures the vestibular response. It has the potential to objectively, quickly and cost-effectively screen concussion. EVestG signals are recorded painlessly and non-invasively from the external ear in response to vestibular stimuli, and consist of brainstem and peripheral sensory oto-acoustic signals modulated by the cortical responses. In this study, we investigated the relationship between characteristic features of the extracted field potentials (FPs) of EVestG signals in people with side-impact concussion in comparison with those of control participants. 10 side-impact concussed individuals (4 Right and 6 left side-impact) and 10 age-and-gender-matched controls were tested by EVestG. The participants also completed comprehensive neuropsychological assessments. Characteristic features were extracted from the FPs during side tilt, and linear discriminant analysis (LDA) classification was applied to the extracted features using a leave-one-out routine. The results show the difference between the left and right FP area was significantly (P<0.05) different. The LDA classification resulted a sensitivity of 85% and specificity of 69% for separating concussed individuals from controls. EVestG appears to have diagnostic potential in diagnosing side impact concussion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".