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Record W2921752812 · doi:10.1097/mao.0000000000002183

Efficacy of Auditory Implants for Patients With Conductive and Mixed Hearing Loss Depends on Implant Center

2019· review· en· W2921752812 on OpenAlexaff
A.F.M. Snik, H. Maier, William Hodgetts, Martin Kompis, Griet Mertens, Paul Van de Heyning, Thomas Lenarz, Arjan J. Bosman

Bibliographic record

VenueOtology & Neurotology · 2019
Typereview
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAudiologyCochlear implantImplantHearing lossConductive hearing lossBone conductionSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Although from a technological point of view, progress is impressive, most implantable hearing devices for conductive or mixed hearing loss have a limited capacity. These devices all bypass the impaired middle ear; therefore, the desired amplification (gain) should be based on the cochlear hearing loss (component) only. The aim of the study is to review the literature with regard to accomplished gain with current implantable devices. METHOD: Thirty-one articles could be included. Aided thresholds were compared with prescribed values, based on cochlear hearing loss (bone-conduction thresholds), according to the well-validated NAL rule. RESULTS: For the majority of the studies, NAL targets were not met. Variation in accomplished gain between implant teams was unacceptably large, largely independent of the type of device that was used. NAL targets were best met at 2 kHz, with worse results at the other frequencies. CONCLUSION: Large variations in reported results were found, which primarily depended on implant center. Based on the analyses, a pragmatic fitting procedure is proposed which should minimize the differences between implant centres.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.053
GPT teacher head0.330
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2019
Admission routes1
Has abstractyes

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