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Record W2914715752 · doi:10.1109/mwscas.2018.8624072

A MEMS Outer Hair Cell (OHC) Implant to Improve Sensorineural Response of a Damaged Cochlea

2018· article· en· W2914715752 on OpenAlexaff
Mahsasadat Seyedbarhagh, Sazzadur Chowdhury, Mehrdad Saif

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCochleaHair cellBasilar membraneCochlear implantMicroelectromechanical systemsFinite element methodMaterials scienceBiomedical engineeringCantileverInner earSensorineural hearing lossAudiologyHearing lossAnatomyMedicineStructural engineeringEngineeringNanotechnologyComposite material

Abstract

fetched live from OpenAlex

This paper presents the design of a MEMS based Outer Hair Cell (OHC) for use as a prosthetic implant on the top of the basilar membrane in the Cochlea to improve the hearing conditions for persons with damaged OHC. The MEMS hair cell (OHC) is designed to have a set of 6 variable length microfabricated cantilever beams that can be excited to deform like normal healthy OHC using electrostatic actuation. A 3D Finite Element Analysis (FEA) of the developed OHC has been carried out using IntelliSuite. The FEA results are in 17% agreement with an analytical model published elsewhere. The design eliminates the use of electrode as used in conventional cochlear implants (CIs) and has the potential to improve the hearing condition for patients suffering from sensory neural hearing loss due to cochlear outer hair cell damage.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.293
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2018
Admission routes1
Has abstractyes

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