The impact of hearing-aid amplification and its integrated tinnitus feature on tinnitus management
Bibliographic record
Abstract
Tinnitus is the potentially debilitating perception of phantom sound with no current cure. Although some management approaches are available, the benefits of hearing aid amplification and its added noiser are uncertain. This study assessed impacts of hearing-aid amplification, and amplification with an added noiser feature, on adults with hearing loss and chronic bothersome tinnitus. Thirty adults [42–75 (Mean = 61.1) years old; 18 males], with mild to moderate sensorineural hearing loss but no previous amplification exposure; and bothersome tinnitus [Tinnitus Functional Index (TFI) baseline scores of at least 20], were randomly assigned to one of two groups in a cross-over study to experience amplification-only, and amplification + noiser for one month each, following one month of no intervention. Study hearing aids were worn minimally for 5-h/day. TFI questionnaires evaluated tinnitus severity before and after each condition. A one-way repeated measures ANOVA with 30 participants revealed a significant effect on TFI across baseline, amplification-only, and amplification + noiser conditions (p = .023), suggesting tinnitus improvement across conditions. Further post-hoc tests indicated lower TFI scores in the amplification + noiser condition (p = 0.048), identifying its distinct benefit. Preliminary analysis suggests benefit of hearing aid amplification, with a prominent advantage of the noiser feature on tinnitus management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".