Predicting Reduced Tinnitus Burden After Cochlear Implantation in Adults
Bibliographic record
Abstract
OBJECTIVE: Explore patient characteristics associated with tinnitus improvement after cochlear implantation. STUDY DESIGN: Retrospective cohort. SETTING: Tertiary referral. PATIENTS: Adults with bilateral severe-to-profound hearing loss and tinnitus. INTERVENTIONS: Unilateral cochlear implantation. RESULTS: From 1996 to 2018, 358 patients endorsed pre-implant tinnitus and had ascertainable tinnitus status at 1-year. Clinically significant improvement in Tinnitus Handicap Inventory (THI) (reduction by at least 7-points) was observed in 262 (73.2%) patients, of whom 155 (59.2%) reported complete resolution. Of the 24 characteristics explored, four were identified as independent predictors of improved tinnitus in logistic regression models. In a multivariable model including identified independent predictors, each 10-percentage point increase in baseline hearing in noise test was associated with an 14% reduction in odds of tinnitus resolution or clinically significant improvement (odds ratio [OR] 0.86, 95% confidence limits [CL] 0.77, 0.96) and preoperative use of a hearing aid in the contralateral ear was associated with a 72% reduction (OR 0.28; 95% CL 0.11, 0.73). Each 10-point increase in baseline Hearing Handicap Inventory for Adults (HHI) score was associated with a 28% increase in odds of tinnitus improvement (OR 1.28; 95% CL 1.07, 1.54). Higher baseline burden of tinnitus was associated with higher odds of tinnitus improvement (OR 1.21 per 10-point THI increase, 95% CL 1.04, 1.40). CONCLUSIONS: Worse residual hearing and higher baseline hearing and tinnitus handicap are associated with higher odds of tinnitus improvement. Expectations of patients seeking reduced tinnitus burden following cochlear implantation should be managed by counselling regarding odds of tinnitus improvement compared to those with similar residual hearing and tinnitus burden.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".