Combined Amplification and Sound Therapy for Individuals With Tinnitus and Coexisting Hearing Loss: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The heterogeneity of tinnitus perception and its impact necessitates a tailor-made management approach in everyone. The current study examined the effects of residual inhibition in combined amplification and sound therapy in individuals with tinnitus and coexisting hearing loss. METHODS: A retrospective analysis was performed on patients with tinnitus and coexisting hearing loss between 2016 and 2019. A total of 72 patients provided with combined amplification and sound therapy were divided into 3 groups based on residual inhibition: (i) complete residual inhibition, (ii) partial residual inhibition, and (iii) negative residual inhibition. Tinnitus severity was measured using the Tinnitus Functional Index before treatment and 1 and 6 months after the intervention. A multilevel mixed-effects model was used to examine the treatment effects including both the main and interaction effects of time and residual inhibition on the tinnitus severity. RESULTS: Of the 72 participants, 55 (76%) and 61 (85%) had clinically significant changes (13 points in Tinnitus Functional Index) at 1-month and 6-month postintervention, respectively. In the complete, partial, and negative residual inhibition groups, the reduction in tinnitus impact was 100%, 78%, and 74%, respectively. A multilevel mixed model analysis showed that the main effects of time and residual inhibition along with their interaction were significant. CONCLUSIONS: The study results suggest that combined amplification and sound therapy is beneficial in individuals with tinnitus and coexisting hearing loss in reducing their tinnitus severity, and this benefit was more in individuals with complete residual inhibition. However, these results need to be further confirmed by controlled trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".