Fit-to-Targets and Aided Speech Intelligibility Index Values for Hearing Aids Fitted to the DSL v5-Adult Prescription
Bibliographic record
Abstract
BACKGROUND: Matching hearing aid output levels to prescribed targets is a component of preferred practice, yet recent normative data on appropriateness of fittings are lacking. Verification measures that assess closeness of fit-to-target include raw deviations from target, root-mean-squared-error (RMSE) deviations from target, and aided Speech Intelligibility Index (SII) values. Establishing normative ranges for these measures may help hearing professionals determine whether a patient's fit-to-targets and/or aided speech audibility is typical for his or her degree of hearing loss. PURPOSE: This article aims to characterize the range of fit-to-target and the range of aided SII associated with hearing aid fittings using the Desired Sensation Level version 5.0 (DSL v5-adult) prescription with adults, considering also hearing aid style, venting, and audiometric characteristics. RESEARCH DESIGN: A descriptive and correlational study of data collected from a retrospective chart review. RESULTS: Hearing aid fittings to 281 ears were compiled. The four-frequency average deviation from target (RMSE) was within ± 5 dB of target in 77% of fittings for mid-level speech. Deviation from targets increased with hearing loss, particularly when the loss is greater than 85 dB hearing level or if the loss was steeply sloping. Venting increased the deviation from targets in the low frequencies. Aided SII values strongly correlated with the participants' hearing thresholds. Clinical ranges for RMSE and aided SII were developed for characterization of fitting outcomes. CONCLUSION: Fitting to DSL v5-adult targets was observed within ± 5 dB absolute deviation, or within 5 dB RMSE, on average for typical adult hearing aid fittings. Confidence intervals for deviation from target and aided SII are proposed.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".