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Record W2805731676 · doi:10.1080/14670100.2018.1481179

Peri-operative electrically evoked auditory brainstem response assessment of facial nerve/cochlea interaction at cochlear implantation

2018· article· en· W2805731676 on OpenAlexaff
Nadine Schart‐Morén, Karin Hallin, Sumit Agrawal, Hanif M. Ladak, Per Eriksson, Hao Li, Helge Rask‐Andersen

Bibliographic record

VenueCochlear Implants International · 2018
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersUppsala UniversitetAkademiska Sjukhuset
KeywordsDehiscenceFacial nerveCochleaMedicineBrainstemCochlear implantAudiologyCochlear nerveElectrocochleographyInternal auditory meatusCochlear implantationHearing lossAnatomySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: Dehiscence between the cochlear otic capsule and the facial nerve canal is a rare and relatively newly described pathology. In cochlear implantation (CI), this dehiscence may lead to adverse electric facial nerve stimulation (FNS) already at low levels, rendering its use impossible. Here, we describe an assessment technique to foresee this complication. METHODS: Pre- and postoperative computed tomography (CT) scans and intraoperative electrically evoked auditory brainstem response (e-ABR) measurements were analyzed in two patients with cochlear-facial dehiscence (CFD). RESULTS: Because of the relatively low resolution, the confirmation of CFD with a clinical CT was difficult. The e-ABR displayed a large potential with 6 and 7.5 ms latency, respectively, which did not occur otherwise. DISCUSSION: Potential strategies to resolve and manage FNS are described. CONCLUSION: Prediction of FNS by assessing the distance between the labyrinthine portion of the facial nerve and the cochlea is difficult using conventional CT scans. A large evoked late myogenic potential at low stimulation levels during intraoperative e-ABR measurement may foresee FNS at CI activation.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.596
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.034
GPT teacher head0.376
Teacher spread0.342 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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