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Record W4311914236 · doi:10.1097/aud.0000000000001290

Vestibular Infant Screening–Flanders: What is the Most Appropriate Vestibular Screening Tool in Hearing-Impaired Children?

2022· article· en· W4311914236 on OpenAlexaboutno aff
Sarie Martens, Leen Maes, Cleo Dhondt, Saartje Vanaudenaerde, Marieke Sucaet, Els De Leenheer, Helen Van Hoecke, Ruth Van Hecke, Lotte Rombaut, Ingeborg Dhooge

Bibliographic record

VenueEar and Hearing · 2022
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsnot available
FundersVlaamse regeringUniversiteit GentFaculty of Medicine and Health, University of SydneyUniversitair Ziekenhuis GentFonds Wetenschappelijk Onderzoek
KeywordsVestibular systemAudiologyMedicineVestibular evoked myogenic potentialHearing loss

Abstract

fetched live from OpenAlex

OBJECTIVES: As children with sensorineural hearing loss have an increased risk for vestibular impairment, the Vestibular Infant Screening-Flanders project implemented a vestibular screening by means of cervical vestibular evoked myogenic potentials (cVEMP) at the age of 6 months for each child with hearing loss in Flanders (Belgium). Given that vestibular deficits can affect the child's development, this vestibular screening should allow early detection and intervention. However, less is currently known about which screening tool would be the most ideal and how vestibular impairment can evolve. Therefore, this study aimed to determine the most appropriate tool to screen for vestibular deficits, to assess the necessity of vestibular follow-up, and to set clinical guidelines for vestibular screening in children with hearing loss. DESIGN: In total, 71 children with congenital or early-onset sensorineural hearing loss were enrolled (mean age at first appointment = 6.7 months). Follow-up was provided at 6 months, 1, 2, and 3 years of age. Below three years of age, the video Head Impulse Test (vHIT) of the horizontal semicircular canals (SCC), the cVEMP, and the rotatory test at 0.16, 0.04, and 0.01 Hz were applied. At 3 years of age, the vHIT of the vertical SCC and ocular vestibular evoked myogenic potentials (oVEMP) were added. To evaluate early motor development, the Alberta Infant Motor Scale (AIMS) results at 6 months and 1-year old were included. RESULTS: At 6 months of age, the highest success rate was obtained with the cVEMP (90.0%) compared to the vHIT (70.0%) and the rotatory test (34.3-72.9%). Overall, vestibular deficits were found in 20.0% of the children, consisting of 13.9% with both SCC and otolith deficits (bilateral: 9.3%, unilateral: 4.6%), and 6.1% with unilateral isolated SCC (4.6%) or otolith (1.5%) deficits. Thus, vestibular deficits would not have been detected in 4.6% of the children by only using the cVEMP, whereas 1.5% would have been missed when only using the vHIT. Although vestibular deficits were more frequently found in severe to profound hearing loss (28.6%), characteristics of vestibular function were highly dependent on the underlying etiology. The AIMS results showed significantly weaker early motor development in children with bilateral vestibular deficits ( p = 0.001), but could not differentiate children with bilateral normal vestibular function from those with unilateral vestibular deficits ( p > 0.05). Progressive or delayed-onset vestibular dysfunction was only found in a few cases (age range: 12-36 months), in which the hearing loss was mainly caused by congenital cytomegalovirus (cCMV). CONCLUSIONS: The cVEMP is the most feasible screening tool to assess vestibular function in 6-months-old children with hearing loss. Although the majority of children with vestibular deficits are detected with the cVEMP, the vHIT seems even more sensitive as isolated SCC deficits are associated with specific etiologies of hearing loss. As a result, the cVEMP is an appropriate vestibular screening tool, which is advised at least in severe to profound hearing loss, but certain etiologies require the addition of the vHIT (i.e., cCMV, meningitis, cochleovestibular anomalies with or without syndromic hearing loss).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.248
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2022
Admission routes1
Has abstractyes

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