Assessing Chronic Ear Symptoms in Bone-Conduction Hearing Implant (BCHI) Patients Using the Chronic Otitis Media Benefit Inventory (COMBI) Score
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine improvement in health-related quality of life (HRQoL) using a validated disease-specific patient-reported outcome measure (PROM) questionnaire in patients undergoing bone-conduction hearing implant (BCHI) insertion. STUDY DESIGN: A mixed retrospective and prospective correlational study. SETTING: Single tertiary referral center in the United Kingdom. PATIENTS: All adult patients undergoing their first BCHI over 6 years (April 1, 2017, to March 3, 2023). MAIN OUTCOME MEASURES: The Chronic Otitis Media Benefit Inventory (COMBI) score (postintervention) and the Glasgow Health Status Inventory (GHSI) (pre-and post-BCHI questionnaire). RESULTS: Improvements were seen across all COMBI domains. The mean total COMBI score was 46.3 (standard deviation = 5.3). Although expected significant improvements were seen in hearing and social domains, there were also notable gains in ear symptoms and reduced medical intervention post-BCHI. There was a statistically significant improvement in all GHSI scores post-BCHI (median total difference 67.1, p < 0.0001). CONCLUSIONS: This study reports very favorable outcomes for BCHI patients using two different PROMs: COMBI and GHSI. Although these PROMs complement each other, they also offer different perspectives on the same cohort of patients, with COMBI providing a unique insight into specific ear symptoms. This is the first reported study using this complement of PROMS in BCHI patients and offers further evidence for the wide-reaching improvements BCHI can have for patients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".