Bone-conduction hearing implants: a potential postcode lottery
Bibliographic record
Abstract
Background Bone conduction hearing implants (BCHIs) are a valuable alternative option for patients with hearing loss when conventional hearing aids are not effective or a viable option. In the UK, specialist sites offer BCHI services. We aimed to understand whether patients face geographical barriers to accessing this healthcare service. Methods A retrospective cohort study was performed at five hospitals in the North West of England over a one-year period (January–December 2023). Results In total, 167 primary BCHIs were implanted (median age, 57.7 years; female, n=52 (31.1%)). Patients travelled a median distance of 17.3km from their home to the BCHI site. Of the patients receiving a BCHI, 108 (64.7%) lived in the locality of a BCHI site. The remaining 59 (35.3%) were referred from a non-BCHI centre. The majority of BCHIs were percutaneous (n=154/167, 92.2%) and were performed under local anaesthetic (n=127/167, 76.0%). No correlation between patient age and distance travelled was identified (p=0.22, R=−0.0951). Conclusions The findings suggest a greater percentage of all BCHIs that are conducted are seen first at a BCHI centre initially rather than seen elsewhere. This could represent a potential geographical barrier to accessing BCHI services for patients not living in the locality of a non-BCHI providing centre. Future work is required to better understand BCHI service barriers on a national level and to identify methods to ensure equitable access for all.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".