Consensus practice parameter: audiological assessment and management of unilateral hearing loss in children
Bibliographic record
Abstract
Objective: Provide recommendations to audiologists for the management of children with unilateral hearing loss (UHL) and for needed research that can lend further insight into important unanswered questions.Design: An international panel of experts on children with UHL was convened following a day and a half of presentations on the same. The evidence reviewed for this parameter was gathered through web-based literature searches specifically designed for academic and health care resources, recent systematic reviews of literature, and new research presented at the conference that underwent peer review for publication by the time of this writing.Study sample: Expert opinions and electronic databases including Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Education Resources Information Centre (ERIC), Google Scholar, PsycINFO, PubMed, ScienceDirect, and Turning Research into Practice (TRIP) Database.Results: The resulting practice parameter requires a personalised, family-centred process: (1) routine surveillance of speech-language, psychosocial, auditory, and academic or pre-academic development; (2) medical assessments for determination of aetiology of hearing loss; (3) assessment of hearing technologies; and (4) considerations for family-centred counselling.Conclusions: This practice parameter provides guidance to clinical audiologists on individualising the management of children with UHL. In addition, the paper concludes with recommendations for research priorities.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".