Venting Corrections Improve the Accuracy of Coupler-Based Simulated Real-Ear Verification for Use with Adult Hearing Aid Fittings
Bibliographic record
Abstract
BACKGROUND: Hearing aid responses can be verified with the Real-Ear Aided Response (REAR). Procedures for predicting the REAR from coupler-based verification exist, but have not incorporated corrections for venting, limiting their use and validity for vented and open fittings. A commercially available system for including venting effects in simulated real-ear measurement (S-REM) has recently been developed. PURPOSE: To evaluate the accuracy of a vent-corrected S-REM for predicting the REAR across test levels, for fittings with a wide range of coupling styles including modular domes. RESEARCH DESIGN: This was a within-subject comparison study using technical measures. Retrospective file review was used to obtain previously measured REARs from 104 fittings in 52 adults and three hearing aid styles. Prospective data collection was used to re-measure each fitting at three test levels using S-REM with and without venting corrections. Comparison of differences by frequency band was performed to assess the impact of the venting correction. RESULTS: The vent model reduced low-frequency error by up to 11 dB, and the effects were consistent with the expected effects of venting in hearing aid fitting: fittings with more open dome or tip styles had a larger improvement when the vent model was added. A larger sample of fittings was obtained for dome/sleeve couplings than for custom fittings. CONCLUSIONS: The vent-corrected S-REM system evaluated in this study provides improved fitting accuracy for dome or sleeve-fitted hearing aids for adults and supports the use of vented S-REM for open fittings. Further studies to examine a representative sample of custom tip or mold fittings, and fittings for children are future directions.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".