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Record W2743652051 · doi:10.1097/mao.0000000000001407

Cochlear Implants Meet Regenerative Biology: State of the Science and Future Research Directions

2017· review· en· W2743652051 on OpenAlexaffabout
Alain Dabdoub, Koji Nishimura

Bibliographic record

VenueOtology & Neurotology · 2017
Typereview
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsCochlear implantMedicineAudiologyCochlear implantationRegenerative medicineHearing lossStem cellBiology

Abstract

fetched live from OpenAlex

: The cochlear implant, the first device to restore a human sense, is an electronic substitute for lost mechanosensory hair cells. It has been successful at providing hearing to people with severe to profound hearing loss and as of 2012, an estimated 324,000 patients worldwide have received cochlear implants. Users of cochlear implants however, suffer from difficulties in processing complex sounds such as music and in discriminating sounds in noisy environments. Recent advances in regenerative biology and medicine are opening new avenues for enhancing the efficacy of cochlear implants by improving the neural interface in the future and offer the possibility of an entirely biological solution for hearing loss in the long term. This report comprises the latest developments presented in the first Symposium on cochlear implants and regenerative biology, held at the 14th International Conference on Cochlear Implants in 2016 in Toronto, Canada.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.004

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 head0.311
GPT teacher head0.489
Teacher spread0.178 · 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 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

Citations15
Published2017
Admission routes2
Has abstractyes

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