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Record W4415790461 · doi:10.1308/rcsann.2025.0074

Bone-conduction hearing implants: a potential postcode lottery

2025· article· en· W4415790461 on OpenAlexaff
Sarah Akbar, Todd H. Davies, N. Walker, G. Thompson, R Fameesh, Abhishek Pahade, Rohini Aggarwal, Sarita Agarwal, A Muddaiah, I Anderco, Elliot Heward

Bibliographic record

VenueAnnals of The Royal College of Surgeons of England · 2025
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsLotteryWork (physics)Service (business)LocalityMEDLINE

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.283
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueAnnals of The Royal College of Surgeons of EnglandSame topicEar Surgery and Otitis MediaFrench-language works237,207