Less invasive SARS-CoV-2 testing for children: A comparison of saliva and a novel Anterior Nasal Swab
Bibliographic record
Abstract
Abstract Reducing procedural discomfort for children requiring respiratory testing for SARS-CoV-2 is important in supporting testing strategies for case identification. Alternative sampling methods to nose and throat swabs, which can be self-collected, may reduce laboratory-based testing requirements and provide rapid results for clearance to attend school or hospital settings. The aim of this study was to compare preference and diagnostic sensitivity of a novel anterior nasal swab (ANS), and saliva, with a standard combined nose and throat (CTN) swab. The three samples were self-collected by children aged 5-18 years who had COVID-19 or were a household close contact. Samples were analysed by reverse transcription polymerase chain reaction (RT-PCR) on the Allplex SARS-CoV-2 Assay. Most children and parents preferred the ANS and saliva swab over the CTN swab for future testing. The ANS was highly sensitive (sensitivity 1.000 (95% Confidence Interval (CI) 0.920, 1.000)) for SARS-CoV-2 detection, compared to saliva (sensitivity 0.886, 95% CI 0.754, 0.962). We conclude the novel ANS is a highly sensitive and more comfortable method for SARS-CoV-2 detection when compared to CTN swab.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".