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Record W2960865931 · doi:10.1016/j.heares.2019.07.004

Measuring temporal response properties of auditory nerve fibers in cochlear implant recipients

2019· article· en· W2960865931 on OpenAlexaff
Sonia Tabibi, Andrea Kegel, Wai Kong Lai, Ian C. Bruce, Norbert Dillier

Bibliographic record

VenueHearing Research · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMcMaster University
FundersSeventh Framework ProgrammeEuropean Commission
KeywordsFacilitationCochlear implantStimulus (psychology)Refractory periodCochleaTime constantRound windowCochlear nerveElectrophysiologyCompound muscle action potentialAudiologyExponential decayMaterials scienceCochlear implantationBiomedical engineeringMedicineNeurosciencePhysicsBiologyAnesthesiaPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Auditory nerve fibers' (ANFs) refractoriness and facilitation can be quantified in electrically evoked compound action potentials (ECAPs) recorded via neural response telemetry (NRT). Although facilitation has been observed in animals and human cochlear implant (CI) recipients, no study has modeled this in human CI users until now. In this study, recovery and facilitation effects at different masker and probe levels for three test electrodes (E6, E12 and E18) in 11 CI subjects were recorded. The ECAP recovery and facilitation were modeled by exponential functions and the same function used for +10 CL masker offset condition can be applied to all other masker offsets measurements. Goodness of fit was evaluated for the exponential functions. A significant effect of probe level was observed on a recovery time constant which highlights the importance of recording the recovery function at the maximum acceptable stimulus level. Facilitation time constant and amplitude showed no dependency on the probe level. However, facilitation was stronger for masker level at or around the threshold of the ECAP (T-ECAP). There was a positive correlation between facilitation magnitude and amplitude growth function (AGF) slope, which indicates that CI subjects with better peripheral neural survival have stronger facilitation.

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)0.005
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.253
GPT teacher head0.371
Teacher spread0.119 · 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 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

Citations17
Published2019
Admission routes1
Has abstractyes

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