Use of Throat Swab or Saliva Specimens for Detection of Respiratory Viruses in Children
Bibliographic record
Abstract
BACKGROUND: Nasopharyngeal (NP) specimens are commonly used for the detection of respiratory viruses, but throat and saliva specimens are easier to obtain. The objective of this study was to compare the viral yield of direct fluorescent antigen detection of NP specimens and nucleic acid amplification tests (NAT) of direct fluorescent antigen-negative NP specimens with the viral yield of NAT of throat swab and saliva specimens. METHODS: NP, throat swab, and saliva specimens were obtained from children and adolescents aged </=17 years. Direct fluorescent antigen testing of the NP specimen for respiratory syncytial virus, influenza A and B viruses, and parainfluenza virus was performed. If no virus was detected, NAT was performed for these 4 viruses, adenovirus, and human metapneumovirus. If a virus was detected by either method, NAT for the same virus was performed for the corresponding throat swab and saliva specimens. RESULTS: A virus was detected in 105 of the 137 NP specimens. The same virus was detectable by NAT in 87 (83%) of 105 throat swab specimens and 77 (74%) of 104 saliva specimens. The likelihood of viral detection among throat swab and saliva swab specimens was higher when the NP specimen tested positive by direct fluorescent antigen testing, compared with NAT alone, and when the specimens were obtained within 3 days after symptom onset, compared with later in the illness. CONCLUSIONS: Throat swab and saliva specimens are inferior to NP specimens for the detection of respiratory viruses but might be acceptable for screening in a setting where it is impractical to obtain an NP specimen.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".