Limited usable hearing unilaterally (LUHU) in infants and young children: a scoping review of technology considerations and developmental outcomes
Bibliographic record
Abstract
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 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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".