Bone‐Anchored Hearing Aid: Why Do Some Patients Refuse It?
Bibliographic record
Abstract
Objective BAHA is a proven tool to improve hearing. Nevertheless, there are patients who are candidates for BAHA implants who end up refusing the surgery. The objective of this study is to review our BAHA experience with particular emphasis on reasons behind the refusal of some candidates. Method A retrospective chart review conducted in December 2011 of the most recent 100 consecutive new patients referred to the BAHA program in a tertiary health care center. Candidates’ demographics, hearing status, co‐morbidities, and audiometeric tests were all recorded. Patients’ acceptance or refusal was noted alongside the reasons to refuse BAHA. Results One hundred new patients were seen for BAHA assessment, 10 patients were excluded because of incomplete tests. There were 68 children and 22 adults. Unilateral conductive hearing loss was the most common reason for consultation (40%), followed by unilateral SNHL (23.3%). Aural atresia was the most common clinical finding (36.6%). Seventy patients were candidates for BAHA (77.8%) as per our candidacy rules. Ten candidates refused BAHA (14.3%). The most common reason for refusal in adults was lack of sound localization in patients with unilateral SNHL. Conclusion The main reason for refusal of BAHA was lack of sound localization in adults, whereas the main reason for refusal in children was cultural and social acceptance by the family. Patients with congenital anomalies were the most likely candidates to accept BAHA implants.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".