Clinical Practice Patterns of Fitting Advanced Device Features in Children With Cochlear Implants
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to identify common clinical practice patterns for providing advanced noise management features in children with cochlear implants (CIs) and evaluate trends in consideration of clinician experience and comfort with CI manufacturer-specific technology. METHOD: A mixed-model survey including quantitative and qualitative questions regarding providing advanced noise management features in the pediatric CI population was collected electronically via research electronic data capture. Survey questions spanned approach/philosophy toward provision of features, age of provision, and demographics of respondents. Descriptive statistics were completed to define common clinical practice patterns and demographic information. RESULTS: A total of 160 pediatric audiologists from 35 U.S. States and five Canadian provinces completed the survey. Most audiologists (73.8%) reported enabling automatic directional microphones, and a vast majority (91%) reported enabling advanced noise processing features such as automatic noise cancellers, wind noise cancellers, and impulse noise cancellers in recipients' main programs. Audiologists ranked features in terms of importance for a school-age child with the top three ranked as automatic noise reduction, automatic directional microphones, and concha-level microphones. Importance of child-specific factors varied depending upon the specific feature of interest. CONCLUSIONS: Variability exists among providers in enabling advanced noise management features for pediatric CI recipients. Multiple factors, including patient characteristics, provider characteristics, and limited evidence-based guidance, could account for much of the variation. Overall, there is a trend toward automaticity for noise management. Additional studies are warranted to provide the evidence base for confidently programming advanced features for children using CIs.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".