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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".