How common are taste and smell abnormalities in COVID-19? A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Olfactory and gustatory dysfunction (OGD) are important early clinical symptoms of COVID-19. We aim to calculate the pooled prevalence of these symptoms and discuss the likely implications on clinical practice such as their use as screening tools and potential prognosis indicators. METHODS: Using a combination of keywords and medical subject headings, we searched for observational studies in the following five databases: Medline/PubMed, Scopus, Cochrane Library, Web of Science, and Google Scholar. Two authors independently screened and selected the final articles according to the inclusion criteria. Two investigators independently assessed the risk of bias in individual studies using the Newcastle-Ottawa Scale. Heterogeneity and publication bias were also assessed. The reported outcome of the pooled analysis was the prevalence of OGD calculated using a random-effect model. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed to report results. RESULTS: Seventeen studies with a total sample size of 4149 were included in this meta-analysis. Out of these, 2106 and 2676 patients reported some degree of olfactory and/or gustatory dysfunction with COVID-19, respectively. The reported outcomes were in terms of pooled prevalence, with gustatory dysfunction being 57.33% and olfactory dysfunction being 59.69%, a significantly high occurrence. CONCLUSION: There is a high occurrence of smell and taste impairment in COVID-19. Given the lack of objective testing for detecting OGD in most studies, the high prevalence found is likely to be an underestimation of the true prevalence. This implies that physicians must use them as reliable early indicators of COVID-19 and employ them before using expensive tests.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.059 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".