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Record W2023724615 · doi:10.1097/moo.0b013e328357a6b3

Recent advances in viral inner ear disorders

2012· review· en· W2023724615 on OpenAlexaff
Jason A. Beyea, Sumit Agrawal, Lorne S. Parnes

Bibliographic record

VenueCurrent Opinion in Otolaryngology & Head & Neck Surgery · 2012
Typereview
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineDiseaseSensorineural hearing lossIntensive care medicinePediatricsHearing lossPopulationImmunologyAudiologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To highlight the recent advances in the understanding of the diagnosis and management of viral inner ear disorders. Congenital sensorineural hearing loss (cSNHL), sudden sensorineural hearing loss (SSNHL), Ménière's disease, and vestibular neuritis/viral labyrinthitis are discussed. RECENT FINDINGS: Cytomegalovirus infection during pregnancy is an under-recognized cause of hearing loss and central nervous system disease amongst the general population. Prevention of maternal infection and treatment of affected newborns with ganciclovir are promising interventions. Recent evidence in SSNHL patients has resulted in recommendations against viral serology or the use of antivirals. There appears to be an increased risk of SSNHL in patients with comorbid hypertension and diabetes. The viral hypothesis of Ménière's disease remains unproven. In patients with an acute episode of vestibular neuritis, there is presently not sufficient evidence to support the routine use of corticosteroids or antiviral medications. SUMMARY: cSNHL remains the most clearly defined of the viral inner ear disorders. The evidence for viral involvement in SSNHL, Ménière's disease, and vestibular neuritis is indirect and equivocal. This review highlights the recent advancements in the diagnosis and management of these disorders.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.134
GPT teacher head0.393
Teacher spread0.259 · 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.

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

Citations26
Published2012
Admission routes1
Has abstractyes

Explore more

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