The accuracy of saliva versus nasopharyngeal and/or oropharyngeal samples for the detection of SARS-CoV-2 in children – A rapid systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The comparative performance of saliva and nasopharyngeal samples for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by reverse transcriptase polymerase chain reaction (RT-PCR) in children remains unclear. As schools reopen around the world, there is an interest in the use of saliva samples for the detection of SARS-CoV-2 in children to circumvent barriers with nasopharyngeal sampling. We systematically reviewed the literature to understand the performance of saliva sampling using RT-PCR on naso- and/or oropharyngeal swabs as the reference standard. Methods Articles from PubMed/MEDLINE and Living Evidence were accessed until 28 th April 2021. A search method without restriction to children population was applied and during the review phase, if a study included patients <18 years old, authors were contacted to provide additional information on the subset of children. Studies were eligible if they reported on matched saliva and naso- and/or oropharyngeal samples, taken from the same patient on the same day. Studies using other respiratory samples such as sputum samples were excluded. Each paired patient sample had to be tested on the same RT-PCR platform. Results Ten studies were included, comprising 1486 matched saliva and on naso- and/or oropharyngeal pairs from children aged 0 to 18 years old. The pooled absolute sensitivity and specificity of saliva sampling using RT-PCR on nasopharyngeal samples as the reference standard was 84.5% (95% CI; 78.0%-90.3%) and 99.5% (95% CI; 98.2%-100.0%). Comparable performance of saliva to nasopharyngeal samples was shown in both symptomatic and asymptomatic children. Stratified analyses of various covariates showed no significant differences. Discussion Our pooled accuracy estimates of RT-PCR SARS-CoV-2 testing on saliva in children did not seem to be different from meta-analyses of studies that enrolled mainly adults. Saliva could potentially be considered an alternative sampling method for screening in children and to pick up those with high viral load.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".