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Record W1917534286 · doi:10.3109/00016489.2015.1067904

Quality standards for bone conduction implants

2015· article· en· W1917534286 on OpenAlexaff
Javiér Gavilán, Oliver F. Adunka, Sumit Agrawal, Marcus D. Atlas, Wolf‐Dieter Baumgartner, Stefan Brill, Iain Bruce, Craig A. Buchman, Marco Caversaccio, Marc De Bodt, Meg Dillon, Benoît Godey, Kevin Green, Rudolf Hagen, Abdulrahman Hagr, De-min Han, Mohan Kameswaran, Eva Karltorp, Martin Kompis, Vlad Kuzovkov, Luis Lassaletta, Yongxin Li, Artur Lorens, Jane Martin, Manikoth Manoj, Griet Mertens, Robert Mlynski, Joachim Mueller, Martin O’Driscoll, Lorne Parnes, Sasidharan Pulibalathingal, Andreas Radeloff, Christopher Raine, Gunesh P. Rajan, Ranjith Rajeswaran, Joachim Schmutzhard, Henryk Skarżyńśki, Piotr H. Skarżyński, Georg Sprinzl, Hinrich Staecker, Kurt Stephan, S. B. Sugarova, Dayse Távora‐Vieira, Shin‐ichi Usami, Yu. К. Yanov, Mario Zernotti, Patrick Zorowka, Paul Van de Heyning

Bibliographic record

VenueActa Oto-Laryngologica · 2015
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineBone conductionQuality (philosophy)Clinical PracticeBest practiceImplantPatient careAccommodationMedical physicsPhysical therapySurgeryNursingAudiologyPsychology

Abstract

fetched live from OpenAlex

CONCLUSION: Bone conduction implants are useful in patients with conductive and mixed hearing loss for whom conventional surgery or hearing aids are no longer an option. They may also be used in patients affected by single-sided deafness. OBJECTIVES: To establish a consensus on the quality standards required for centers willing to create a bone conduction implant program. METHOD: To ensure a consistently high level of service and to provide patients with the best possible solution the members of the HEARRING network have established a set of quality standards for bone conduction implants. These standards constitute a realistic minimum attainable by all implant clinics and should be employed alongside current best practice guidelines. RESULTS: Fifteen items are thoroughly analyzed. They include team structure, accommodation and clinical facilities, selection criteria, evaluation process, complete preoperative and surgical information, postoperative fitting and assessment, follow-up, device failure, clinical management, transfer of care and patient complaints.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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)0.0000.000

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 head0.130
GPT teacher head0.384
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 designNot applicable
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

Citations30
Published2015
Admission routes1
Has abstractyes

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