A Critical Review of Audiological Outcome Measures for Infants and Children
Bibliographic record
Abstract
Outcome evaluation is an important stage in the pediatric hearing aid fitting process, however a systematic way of evaluating outcome in the pediatric audiology population is lacking. This is in part due to the need for an evidence-based outcome evaluation guideline for infants and children with hearing loss who wear hearing aids. As part of the development of a guideline, a critical review of the existing pediatric audiology outcome evaluation tools was conducted. Subjective outcome evaluation tools that measure auditory-related behaviors in children from birth to 6 years of age were critically appraised using a published grading system (Andresen, 2000). Of the tools that exist, 12 were appraised because they met initial criteria outlined by the Network of Pediatric Audiologists of Canada as being appropriate for children birth to 6 years of age who wear hearing aids. Tools that were considered for the guideline scored high in both statistical and feasibility criteria. The subjective outcome evaluation tools that were ultimately chosen to be included in the guideline were the LittlEARS Auditory Questionnaire (Tsiakpini et al., 2004) and the Parents' Evaluation of Aural/Oral Performance of Children (PEACH) Rating Scale (Ching & Hill, 2005b) due to the high grades they received in the critical review and their target age ranges. Following this critical review of pediatric outcome evaluation tools, the next step was for the Network Clinicians to evaluate the guideline (Moodie et al., 2011b).
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".