Candidacy for Amplification in Children With Hearing Loss: A Review of Guidelines and Recommendations
Bibliographic record
Abstract
Purpose The 1st point in the intervention process for the majority of children is the fitting of hearing devices. The objective of this review was to compile guidelines and recommendations for candidacy criteria for children with hearing loss. Method Electronic databases (e.g., MEDLINE, Embase, and Cumulative Index of Nursing and Allied Health Literature) and websites were searched. Any document referring to children with hearing loss that discussed amplification guidelines or protocols was included. Documents specific to implantable devices or addressing only remote microphone systems were excluded. One reviewer screened all potentially relevant documents, and a subset was screened by a 2nd reviewer. Guidelines/recommendations referring to pediatric amplification candidacy were extracted. Results A total of 40 documents were included for data extraction. Studies were categorized according to hearing loss of any degree, with separate categories for documents providing specific criteria for mild bilateral, unilateral, and auditory neuropathy spectrum disorders. Guidelines ranged from generic statements about the need for amplification to criteria based on specific audiometric thresholds. In guidelines recommending audiometric cut-points, the majority considered > 25 dB HL as a criterion for consideration for amplification. Overall, guidelines for children with mild bilateral and unilateral loss remain more ambiguous, and there was some variation across the recommendations. Guidelines for auditory neuropathy spectrum disorder stressed the need to obtain results from behavioral audiometry before considering amplification. Conclusions Numerous organizations have established candidacy guidelines for pediatric amplification. Most guidelines specify criteria for amplification as audiometric threshold levels. There is considerable variation in the guidelines for mild bilateral and unilateral hearing loss with candidacy criteria ranging from 15 to 30 dB HL, and many guidelines recommend a case-by-case decision approach.
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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".