Performing and analysing tone-induced cervical and ocular vestibular-evoked myogenic potentials in traumatic and non-traumatic vestibular pathology
Bibliographic record
Abstract
OBJECTIVE: This paper discusses our technique of carrying out cervical and ocular vestibular-evoked myogenic potential testing in a single position. The described technique allows for a symmetrical, natural flexion of the neck muscles, which is helpful as many of our patients have suffered traumatic deceleration injures. METHODS: Patients with suspected vestibular pathology referred by specialists were sequentially assessed in a tertiary referral neuro-otology unit within a teaching hospital using our technique and our previously established normative database. All patients underwent standardised vestibular assessment in addition to cervical and ocular vestibular-evoked myogenic potential assessment. Our normative data are in keeping with that reported by other centres. RESULTS: Many of the patients had abnormal vestibular-evoked myogenic potentials, which is in line with a history suggesting otolithic disease. CONCLUSION: Both cervical and ocular vestibular-evoked myogenic potentials offer several parameters for detecting abnormalities. The technique reported enables us to assess patients in an accurate fashion whether or not they have suffered traumatic neck injuries.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".