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Record W2589944099 · doi:10.4081/audiores.2017.168

A Retrospective Study of the Clinical Characteristics and Post-Treatment Hearing Outcome in Idiopathic Sudden Sensorineural Hearing Loss

2017· article· en· W2589944099 on OpenAlexaff
Purushothaman Ganesan, Purushothaman Pavanjur Kothandaraman, Simham Swapna, Vinaya Manchaiah

Bibliographic record

VenueAudiology Research · 2017
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsSaskatoon Medical Imaging
Fundersnot available
KeywordsMedicineWilcoxon signed-rank testAudiologyRetrospective cohort studyHearing lossTinnitusSensorineural hearing lossMann–Whitney U testSurgeryInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this retrospective study was to analyze the clinical characteristics and document hearing recovery in patients with idiopathic sudden sensorineural hearing loss (ISSNHL). 122 patients diagnosed with unilateral ISSNHL, from March 2009 to December 2014, were treated with oral steroids and pentoxifylline. Hearing change was evaluated by comparing pre-treatment and post-treatment pure-tone average (PTA) (500, 1K, and 2K Hz), and categorized into complete, partial, and no recovery of hearing. T-test, Wilcoxon Signed Rank test and Regression analysis were employed to analyze the statistical significance. Of the 122 patients, seventy-one (58%) had complete recovery and 34 (28%) had partial recovery. The average pre-treatment PTA was 78.3±16.9 dB whereas post-treatment average was 47.0±20.8 dB, showing statistically significant improvement (t=24.89, P≤0.001). The factors such as presence of tinnitus (P=0.005) and initial milder hearing loss (P=0.005) were found to be significant predictors for hearing recovery. Conventional steroid regimes produced a recovery rate in ISSNHL, which exceeds the spontaneous recovery rate. The current study results highlight the importance of medical treatment in the management of ISSNHL.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.259
GPT teacher head0.472
Teacher spread0.213 · 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 designObservational
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

Citations19
Published2017
Admission routes1
Has abstractyes

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