The Effects of Vestibular Implants and Other Existing Treatment Options of Individuals with Bilateral Vestibular Hypofunction: A Review
Bibliographic record
Abstract
Vestibular dysfunction (VD) is an experience that is shared by nearly 35% of Americans above the age of 40 (nearly 69 million). This percentage increases to 80% when looking at the population of individuals over the age of 80. VD can have tremendously negative health outcomes for patients. Improper vestibular function can result in a loss of balance and patients often cite vertigo as a common symptom, which is often associated with dramatically reduced quality of life. Falls are another common clinically significant outcome of VD and place a huge social and financial burden on the patient and healthcare system. Although the pathophysiology and treatment options for certain vestibular disorders have been well researched, disorders such as bilateral vestibular dysfunction (BVD), have had traditionally ineffective treatment options. That said, novel therapeutics such as vestibular implants (VIs) have been recently tested and showed positive health outcomes for patients with BVD. This review aims to investigate the health outcomes of patients who have received VIs, as well as discuss the limitations and existing treatment options.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".