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Record W4413950776 · doi:10.1080/14992027.2025.2549465

Limited usable hearing unilaterally (LUHU) in infants and young children: a scoping review of technology considerations and developmental outcomes

2025· review· en· W4413950776 on OpenAlexaff
Christine Brown, Marlene Bagatto

Bibliographic record

VenueInternational Journal of Audiology · 2025
Typereview
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsWestern University
Fundersnot available
KeywordsAudiologyUSablePsychologyHearing lossMedicineComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this scoping review was to examine the developmental impact of limited usable hearing unilaterally (LUHU) and surgical and non-surgical technology outcomes specific to infants and young children who have LUHU. DESIGN: The Joanna Briggs Institute (JBI) Model of Evidence-Based Healthcare provided a framework. Covidence software was used to manage the articles. Literature searches were conducted in November 2022 and May 2023. Three research audiologists screened the articles followed by full text review by the authors. RESULTS: The searches resulted in 2953 articles. After removal of duplicates, 888 abstracts were screened. 429 articles underwent full text review. Various selection criteria were applied leaving 66 articles for extraction. CONCLUSIONS: The developmental impact of LUHU is comparable to unilateral hearing loss in general. Hearing-related quality of life and listening fatigue are also impacted. Management counselling to review the various technology options should be guided by Magnetic Resonance Imaging (MRI) results. Bilateral listening benefits may be achieved through cochlear implantation. A remote microphone (RM) system, coupled to the normal hearing ear can improve performance in settings where noise and localisation are problematic. A bone conduction device (BCD) or contralateral routeing of signal (CROS) system may mitigate head shadow effects.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.869
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.366
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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