MétaCan
Menu
Back to cohort
Record W2995695250 · doi:10.3389/fneur.2019.01294

Prevalence of Vestibular Dysfunction in Children With Neurological Disabilities: A Systematic Review

2019· review· en· W2995695250 on OpenAlexaff
Shashank Ghai, Mireille Hakim, Elizabeth Dannenbaum, Anouk Lamontagne

Bibliographic record

VenueFrontiers in Neurology · 2019
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsConcordia UniversityMcGill UniversityJewish Rehabilitation Hospital
Fundersnot available
KeywordsMedicineCerebral palsyCINAHLPediatricsAudiologyMEDLINEPhysical therapyPhysical medicine and rehabilitationPsychiatryPsychological intervention

Abstract

fetched live from OpenAlex

Background: In children with neurological or neurodevelopmental conditions, vestibular disorders may co-exist with the primary condition and further contribute to disability and restriction in functional independence and participation. Awareness of their existence may favor an early diagnosis and better treatment outcomes. Objectives: To determine the prevalence of vestibular dysfunction in children and adolescents (3-21 years old) diagnosed with either cerebral palsy (CP), traumatic brain injury (TBI), sensorineural hearing loss (SNHL) or cochlear implantations (CI). Methods: Four researchers systematically reviewed the literature from three databases (EMBASE, MEDLINE, CINAHL) until June 2018. Results: Twenty-four studies were analyzed in this systematic review. A single, high-quality study reports a prevalence of 48.4% of spastic CP children having a saccular dysfunction. Three fair-quality studies report a prevalence of 14.6%-81%, 21 days post-TBI. Twelve poor-to-high quality studies demonstrate a prevalence of 18.7%-96.1% in children with SNHL. A prevalence range of 3%-84% of children with CI is reported by 9 fair-to-high quality studies. Conclusion: Clinicians should be aware of the prevalence of vestibular dysfunction in these populations and implement appropriate assessments to improve treatment outcomes.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations29
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in NeurologySame topicVestibular and auditory disordersFrench-language works237,207