Hearing Loss, Tinnitus, and Dizziness in COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Extensive studies indicate that severe acute respiratory syndrome coronavirus (SARS-CoV-2) involves human sensory systems. A lack of discussion, however, exists given the auditory-vestibular system involvement in CoV disease 2019 (COVID-19). The present systematic review and meta-analysis were performed to determine the event rate (ER) of hearing loss, tinnitus, and dizziness caused by SARS-CoV-2. METHODS: Databases (PubMed, ScienceDirect, Wiley) and World Health Organization updates were searched using combined keywords: 'COVID-19,' 'SARS-CoV-2,' 'pandemic,' 'auditory dysfunction,' 'hearing loss,' 'tinnitus,' 'vestibular dysfunction,' 'dizziness,' 'vertigo,' and 'otologic symptoms.' RESULTS: Twelve papers met the eligibility criteria and were included in the study. These papers were single group prospective, cross-sectional, or retrospective studies on otolaryngologic, neurologic, or general clinical symptoms of COVID-19 and had used subjective assessments for data collection (case histories/medical records). The results of the meta-analysis demonstrate that the ER of hearing loss (3.1%, CIs: 0.01-0.09), tinnitus (4.5%, CIs: 0.012-0.153), and dizziness (12.2%, CIs: 0.070-0.204) is statistically significant in patients with COVID-19 (Z ≤ -4.469, p ≤ 0.001). CONCLUSIONS: COVID-19 can cause hearing loss, tinnitus, and dizziness. These findings, however, should be interpreted with caution given insufficient evidence and heterogeneity among studies. Well-designed studies and follow-up assessments on otologic symptoms of SARS-CoV-2 using standard objective tests are recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".