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Record W4393061124 · doi:10.2196/54326

Exploring the Prevalence of Tinnitus and Ear-Related Symptoms in China After the COVID-19 Pandemic: Online Cross-Sectional Survey

2024· article· en· W4393061124 on OpenAlexvenueno aff
Di Wang, Peifan Li, Xiaoling Huang, Yixuan Liu, Shihang Mao, H. Yin, Na Wang, Yan Luo, Shan Sun

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsnot available
FundersScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of ChinaShanghai Municipal Health Commission
KeywordsTinnitusPandemicCoronavirus disease 2019 (COVID-19)MedicineChinaCross-sectional study2019-20 coronavirus outbreakAudiologyThroatSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PopulationNoseDiseaseGeographyEnvironmental healthVirologyPathologyOutbreakInfectious disease (medical specialty)Surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Tinnitus is a complex and heterogeneous disease that has been identified as a common manifestation of COVID-19. To gain a comprehensive understanding of tinnitus symptoms in individuals following COVID-19 infection, we conducted an online survey called the China Ear Nose and Throat Symptom Survey in the COVID-19 Pandemic (CENTSS) among the Chinese population. OBJECTIVE: Our objective was to investigate tinnitus and ear-related symptoms after COVID-19 infection in the Chinese population, with the aim of providing a solid empirical foundation for improved health care. The findings from CENTSS can contribute to the development of enhanced management strategies for tinnitus in the context of long COVID. By gaining a better understanding of the factors contributing to tinnitus in individuals with COVID-19, health care providers can tailor interventions to address the specific needs of affected patients. Furthermore, this study serves as a basis for research on the long-term consequences of COVID-19 infection and its associated tinnitus symptoms. METHODS: A quantitative, online, cross-sectional survey study design was used to explore the impact of the COVID-19 pandemic on experiences with tinnitus in China. Data were collected through an online questionnaire designed to identify the presence of tinnitus and its impacts. Descriptive statistics were used to analyze individuals' demographic characteristics, COVID-19 infection-related ear symptoms, and the cognitive and emotional implications of tinnitus. Univariable and multivariable logistic regression analyses were used to model the cross-sectional baseline associations between demographic characteristics, noise exposure, educational level, health and lifestyle factors, and the occurrence of tinnitus. RESULTS: Between December 19, 2022, and February 1, 2023, we obtained responses from 1262 Chinese participants representing 24 regions, with an average age of 37 years. Among them, 540 patients (42.8%) reported experiencing ear-related symptoms after COVID-19 infection. Only 114 (9%) of these patients sought medical attention specifically for their ear symptoms, while 426 (33.8%) did not seek hospital care. Tinnitus emerged as the most prevalent and impactful symptom among all ear-related symptoms experienced after COVID-19 infection. Of the respondents, female participants (688/888, 77.78%), younger individuals (<30 years), individuals with lower education levels, participants residing in western China, and those with a history of otolaryngology diseases were more likely to develop tinnitus following COVID-19 infection. CONCLUSIONS: In summary, tinnitus was identified as the most common ear-related symptom during COVID-19 infection. Individuals experiencing tinnitus after COVID-19 infection were found to have poorer cognitive and emotional well-being. Different ear-related symptoms in patients post-COVID-19 infection may suggest viral invasion of various parts of the ear. It is therefore crucial to monitor and manage hearing-related changes resulting from COVID-19 as clinical services resume.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.314
GPT teacher head0.472
Teacher spread0.158 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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