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Record W2044080111 · doi:10.1177/0194599812451426a235

Bone‐Anchored Hearing Aid: Why Do Some Patients Refuse It?

2012· article· en· W2044080111 on OpenAlexaff
Faisal Zawawi, Ghassan Kabbach, Marie Lallemand, Sam J. Daniel

Bibliographic record

VenueOtolaryngology · 2012
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineTertiary careHearing aidCandidacyPediatricsAudiologyGeneral surgery

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.001

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.

Opus teacher head0.021
GPT teacher head0.265
Teacher spread0.244 · 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.

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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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