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Record W4311234357 · doi:10.3390/diagnostics12123139

Sudden Sensorineural Hearing Loss in the COVID-19 Pandemic: A Systematic Review and Meta-Analysis

2022· review· en· W4311234357 on OpenAlex
Andrea Frosolini, Leonardo Franz, Antonio Daloiso, Cosimo de Filippis, Gino Marioni

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDiagnostics · 2022
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity Health Network
FundersUniversità degli Studi di Padova
KeywordsMedicineMeta-analysisFunnel plotCoronavirus disease 2019 (COVID-19)PandemicPublication biasSystematic reviewEpidemiologyIncidence (geometry)Web of sciencePediatricsMEDLINEAudiologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Accumulating data indicate that patients with COVID-19 could be affected by sudden sensorineural hearing loss (SSNHL). The aim of the study was to analyze the epidemiological and clinical trend of SSNHL occurrence during the COVID-19 pandemic by applying a systematic review and meta-analysis approach. METHODS: PubMed, Scopus, Web of Science, ScienceDirect, and Cochrane databases were searched. RESULTS: The seven included studies had adequate relevance to the topic and the quality was fair. The mean age at SSNHL onset ranged from 39.23 to 62.18 years during the pandemic year period (PYP); a meta-analysis of four studies comparing these data with those of previous periods in the same institutions found a younger age during the PYP (pooled mean -0.2848). The heterogeneity was high (76.1935%) and no frank asymmetry was observed in the funnel plot. The SARS-CoV-2 positivity rate of SSNHL patients ranged from 0% to 57.53%. Standard steroid treatments were applied without significant adverse effects. Comprehensively, hearing improvement was achieved for more than half of the cases. No studies reported long-term follow-up data. CONCLUSIONS: Further prospective analyses on large series and a long-term follow up on COVID-related SSNHL cases are necessary to address the open questions regarding the causative link between COVID-19 infection and SSNHL.

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.

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)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.343
GPT teacher head0.418
Teacher spread0.075 · 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