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Record W1541123999 · doi:10.3389/fnsys.2015.00094

The olivocochlear system and protection from acoustic trauma: a mini literature review

2015· review· en· W1541123999 on OpenAlexaff
Adrián Fuente

Bibliographic record

VenueFrontiers in Systems Neuroscience · 2015
Typereview
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAudiologyHearing lossNoise exposureCochleaNoise-induced hearing lossAcoustic traumaHuman studiesMedicineFunction (biology)Noise (video)BiologyComputer scienceInternal medicineGenetics

Abstract

fetched live from OpenAlex

Large intersubject variability in the susceptibility to noise-induced hearing loss (NIHL) is known to occur in both humans and animals. It has been suggested that the olivocochlear system (OCS) plays a significant role in protecting the cochlea from exposure to high levels of noise. A mini literature review about the scientific evidence from animal and human studies about the association between the function of the OCS and susceptibility to NIHL was carried out. Animal data consistently show that de-efferented ears exhibit larger temporary threshold shift (TTS) and permanent threshold shift (PTS) than efferented ears. Data from human studies do not consistently show a correlation between the strength of the OCS function and amount of TTS. Further research on human subjects is required to determine how the OCS function could be used to predict susceptibility to NIHL in individual subjects.

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)0.001
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.072
GPT teacher head0.310
Teacher spread0.238 · 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

Citations35
Published2015
Admission routes1
Has abstractyes

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