Bilateral vestibular hypofunction in children
Bibliographic record
Abstract
BACKGROUND: Vestibular hypofunction in children can lead to frequent falls and delayed motor development. Especially children with bilateral vestibular hypofunction (BVH) are most at risk for developing symptoms. These children might benefit from future therapies, like vestibular implants, to restore their vestibular system. This study aimed to describe the prevalence, etiology, motor development, and hearing status of pediatric patients with BVH. METHODS: A multicenter retrospective chart review of 492 children with sensorineural hearing loss was performed. Children with at least one bilaterally abnormal vestibular test were defined as having a BVH. RESULTS: BVH was found in 23 % of the screened children. Especially children with syndromic hearing loss like Usher, CHARGE, or Waardenburg syndrome and infectious etiologies like congenital CMV and meningitis, were prone to have BVH on all performed tests. Children with BVH had a high percentage of motor developmental delay (81 %), especially if all tests were abnormal on both sides (97 %). CONCLUSION: It is recommended to perform vestibular screening in children with sensorineural hearing loss, as BVH is prevalent. BVH has a very high risk of causing a delay in motor development. Especially in children with BVH on all vestibular tests, motor development is impaired. Those children might benefit from vestibular implants in the future.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".