Self‐reported olfactory and gustatory dysfunction and psychophysical testing in screening for COVID‐19: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: A substantial proportion of coronavirus disease-2019 (COVID-19) patients demonstrate olfactory and gustatory dysfunction (OGD). Self-reporting for OGD is widely used as a predictor of COVID-19. Although psychophysical assessment is currently under investigation in this role, the sensitivity of these screening tests for COVID-19 remains unclear. In this systematic review we assess the sensitivity of self-reporting and psychophysical tests for OGD. METHODS: A systematic search was performed on PubMed, EMBASE, and ClinicalTrials.gov from inception until February 16, 2021. Studies of suspected COVID-19 patients with reported smell or taste alterations were included. Data were pooled for meta-analysis. Sensitivity, specificity, and diagnostic odds ratio (DOR) were reported in the outcomes. RESULTS: In the 50 included studies (42,902 patients), self-reported olfactory dysfunction showed a sensitivity of 43.9% (95% confidence interval [CI], 37.8%-50.2%), a specificity of 91.8% (95% CI, 89.0%-93.9%), and a DOR of 8.74 (95% CI, 6.67-11.46) for predicting COVID-19 infection. Self-reported gustatory dysfunction yielded a sensitivity of 44.9% (95% CI, 36.4%-53.8%), a specificity of 91.5% (95% CI, 87.7%-94.3%), and a DOR of 8.83 (95% CI, 6.48-12.01). Olfactory psychophysical tests analysis revealed a sensitivity of 52.8% (95% CI, 25.5%-78.6%), a specificity of 88.0% (95% CI, 53.7%-97.9%), and a DOR of 8.18 (95% CI, 3.65-18.36). One study used an identification test for gustatory sensations assessment. CONCLUSION: Although demonstrating high specificity and DOR values, neither self-reported OGD nor unvalidated and limited psychophysical tests were sufficiently sensitive in screening for COVID-19. They were not suitable adjuncts in ruling out the disease.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| 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".