Salivary SARS-CoV-2 RNA for diagnosis of COVID-19 patients: A systematic review and meta-analysis of diagnostic accuracy
Bibliographic record
Abstract
Accurate, self-collected, and non-invasive diagnostics are critical to perform mass-screening diagnostic tests for COVID-19. This systematic review with meta-analysis evaluated the accuracy, sensitivity, and specificity of salivary diagnostics for COVID-19 based on SARS-CoV-2 RNA compared with the current reference tests using a nasopharyngeal swab (NPS) and/or oropharyngeal swab (OPS). An electronic search was performed in seven databases to find COVID-19 diagnostic studies simultaneously using saliva and NPS/OPS tests to detect SARS-CoV-2 by RT-PCR. The search resulted in 10,902 records, of which 44 studies were considered eligible. The total sample consisted of 14,043 participants from 21 countries. The accuracy, specificity, and sensitivity for saliva compared with the NPS/OPS was 94.3% (95%CI= 92.1;95.9), 96.4% (95%CI= 96.1;96.7), and 89.2% (95%CI= 85.5;92.0), respectively. Besides, the sensitivity of NPS/OPS was 90.3% (95%CI= 86.4;93.2) and saliva was 86.4% (95%CI= 82.1;89.8) compared to the combination of saliva and NPS/OPS as the gold standard. These findings suggest a similarity in SARS-CoV-2 RNA detection between NPS/OPS swabs and saliva, and the association of both testing approaches as a reference standard can increase by 3.6% the SARS-CoV-2 detection compared with NPS/OPS alone. This study supports saliva as an attractive alternative for diagnostic platforms to provide a non-invasive detection of SARS-CoV-2.
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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 | 0.006 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.005 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".