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Record W2023613221 · doi:10.1017/s0022215108001783

Sensorineural hearing loss in systemic lupus erythematosus: case report and literature review

2008· review· en· W2023613221 on OpenAlexaff
Nader Khalidi, Ryan Rebello, D D Robertson

Bibliographic record

VenueThe Journal of Laryngology & Otology · 2008
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAzathioprinePrednisoneSensorineural hearing lossHearing lossAudiogramSystemic diseaseSystemic lupus erythematosusMagnetic resonance imagingDermatologyLupus erythematosusAudiologyPathologyInternal medicineImmunologyImmunopathologyRadiologyDiseaseAntibody

Abstract

fetched live from OpenAlex

OBJECTIVES: We present a case of systemic lupus erythematosus with symptomatic sensorineural hearing loss which was successfully treated with azathioprine, as assessed both clinically and radiologically. We also present a review of the relevant literature. CASE REPORT: A woman with systemic lupus erythematosus presented with sensorineural hearing loss, initially on the right and subsequently developing on the left over several months. An audiogram revealed profound neurosensory hearing loss bilaterally. The patient was treated with prednisone 60 mg daily and azathioprine 200 mg daily. An improvement on the left was noted on follow-up audiography as well as on magnetic resonance imaging of the internal auditory canals and surrounding structures. CONCLUSION: Sensorineural hearing loss has been described in autoimmune disorders but is rare. Aural symptoms have been described, with varying incidences (0-57.5 per cent), in systemic lupus erythematosus. However, symptomatic sensorineural hearing loss is rare in systemic lupus erythematosus. Prednisone appears essential when an immunological or vasculitic cause is found. The use of azathioprine should be considered, as well as follow-up with magnetic resonance imaging to detect improvement.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.130
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.038
GPT teacher head0.323
Teacher spread0.285 · 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 designCase report
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
Published2008
Admission routes1
Has abstractyes

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