RT-qPCR assays based on saliva rather than on nasopharyngeal swabs are possible but should be interpreted with caution: results from a systematic review and meta-analysis.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE WORK: The ongoing pandemic has elicited an increasing interest regarding the SARS-CoV-2 viral RNA detection in saliva specimens rather than through nasopharyngeal swabs. Our aim was to conduct a meta-analysis on the sensitivity and specificity of SARS-CoV-2 viral RNA detection through RT-qPCR based on salivary specimens compared to conventional nasopharyngeal swabs. METHODS: We reported our meta-analysis according to the PRISMA statement. We searched Pubmed, Embase, and pre-print archive medRxiv.og for eligible studies published up to June 1st, 2020. Raw data included true/false positive and negative tests, and the total number of tests. Sensitivity and specificity data were calculated for every study, and then pooled in a random-effects model. Heterogeneity was assessed using the I2 measure. Reporting bias was assessed by means of funnel plots and regression analysis. RESULTS: The systematic review eventually retrieved 14 studies including a total of 15 estimates, the were included in quantitative synthesis. We found a pooled specificity of 97.7% (95%CI 93.8-99.2) and a pooled sensitivity of 83.4% (95%CI 73.1-90.4), with an overall agreement assessed by means of Cohen's kappa equals to 0.750, 95%CI 0.62-0.88 (i.e. moderate agreement), with high heterogeneity and risk of reporting bias. CONCLUSIONS: In conclusion, diagnostic tests based on salivary specimens are somewhat reliable, but relatively few studies have been carried out. Moreover, such studies are characterized by low numbers and low sample power. Therefore, the of salivary samples is currently questionable for clinical purposes and cannot substitute other more conventional RT-qPCR based on nasopharyngeal swabs.
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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 | 0.053 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.041 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".