Evaluation of Hearing Aid Manufacturers’ Software- Derived Fittings to DSL v5.0 Pediatric Targets
Bibliographic record
Abstract
Background: Hearing aid prescriptive methods are a commonly recommended component of evidencebasedpreferred practice guidelines and are often implemented in the hearing aid programming software.Previous studies evaluating hearing aid manufacturers’ software-derived fittings to prescriptionshave shown significant deviations from targets. However, few such studies examined the accuracy ofsoftware-derived fittings for the Desired Sensation Level (DSL) v5.0 prescription. Purpose: The purpose of this study was to evaluate the accuracy of software-derived fittings to the DSLv5.0 prescription, across a range of hearing aid brands, audiograms, and test levels. Research Design: This study is a prospective chart review with simulated cases. Data Collection and Analysis: A set of software-derived fittings were created for a six-month-old testcase, across audiograms ranging from mild to profound. The aided output from each fitting was verified inthe test box at 55-, 65-, 75-, and 90-dB SPL, and compared with DSL v5.0 child targets. The deviationsfrom target across frequencies 250–6000 Hz were calculated, together with the root-mean-square error(RMSE) from target. The aided Speech Intelligibility Index (SII) values generated for the speech passagesat 55- and 65-dB SPL were compared with published norms. Study Sample: Thirteen behind-the-ear style hearing aids from eight manufacturers were tested. Results: The amount of deviation per frequency was dependent on the test level and degree of hearingloss. Most software-derived fittings for mild-to-moderately severe hearing losses fell within ±5 dB of thetarget for most frequencies. RMSE results revealed more than 84 percent of those hearing aid fittings for themild-to-moderate hearing losses were within 5 dB at all test levels. Fittings for severe to profound hearinglosses had the greatest deviation from target and RMSE. Aided SII values for the mild-to-moderate audiogramsfell within the normative range for DSL pediatric fittings, although they fell within the lower portionof the distribution. For more severe losses, SII values for some hearing aids fell below the normative range. Conclusions: In this study, use of the software-derived manufacturers’ fittings based on the DSL v5.0pediatric targets set most hearing aids within a clinically acceptable range around the prescribed target,particularly for mild-to-moderate hearing losses. However, it is likely that clinician adjustment based onverification of hearing aid output would be required to optimize the fit to target, maximize aided SII, andensure appropriate audibility across all degrees of hearing loss.
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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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".