Saliva as a diagnostic specimen for SARS‐CoV‐2 detection: A scoping review
Bibliographic record
Abstract
OBJECTIVES: This scoping review aims to summarize the diagnostic value of saliva assessed from current studies that (1) compare its performance in reverse transcriptase-polymerase chain reaction testing to nasopharyngeal swabs, (2) evaluate its performance in rapid and point-of-care COVID-19 diagnostic tests, and (3) explore its use as a specimen for detecting anti-SARS-CoV-2 antibodies. MATERIALS AND METHODS: A systematic search was performed on the following databases: Medline and Embase (Ovid), World Health Organization, Centers for Disease Control and Prevention, and Global Health (Ovid) from January 2019 to September 2021. Of the 657 publications identified from the searches, n = 146 articles were included in the final scoping review. RESULTS: Our findings showcase that salivary samples exceed nasopharyngeal swabs in detecting SARS-CoV-2 using reverse transcriptase-polymerase chain reaction testing in several studies. A select number of rapid antigen and point-of-care tests from the literature were also identified capable of high detection rates using saliva. Moreover, anti-SARS-CoV-2 antibodies have been shown to be detectable in saliva through biochemical assays. CONCLUSION: We highlight the potential of saliva as an all-rounded specimen in detecting SARS-CoV-2. However, future large-scale clinical studies will be needed to support its widespread use as a non-invasive clinical specimen for COVID-19 testing.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".