One Swab Fits All: Performance of a Rapid, Antigen-Based SARS-CoV-2 Test Using a Nasal Swab, Nasopharyngeal Swab for Nasal Collection, and RT–PCR Confirmation from Residual Extraction Buffer
Bibliographic record
Abstract
BACKGROUND: Point-of-care SARS-CoV-2 antigen tests have great potential to help combat the COVID-19 pandemic. In the performance of a rapid, antigen-based SARS-CoV-2 test (RAT), our study had 3 main objectives: to determine the accuracy of nasal swabs, the accuracy of using nasopharyngeal swabs for nasal collection (nasalNP), and the effectiveness of using residual extraction buffer for real-time reverse-transcriptase PCR (RT-PCR) confirmation of positive RAT (rPan). METHODS: Symptomatic adults recently diagnosed with COVID-19 in the community were recruited into the study. Nasal samples were collected using either a nasalNP or nasal swab and tested immediately with the RAT in the individual's home by a health care provider. 500 µL of universal transport media was added to the residual extraction buffer after testing and sent to the laboratory for SARS-CoV-2 testing using RT-PCR. Parallel throat swabs tested with RT-PCR were used as the reference comparators. RESULTS: One hundred and fifty-five individuals were included in the study (99 nasal swabs, 56 nasalNP). Sensitivities of nasal samples tested on the RAT using either nasal or nasalNP were 89.0% [95% confidence interval (CI) 80.7%-94.6%] and 90.2% (95% CI 78.6%-96.7%), respectively. rPan positivity agreement compared to throat RT-PCR was 96.2%. CONCLUSIONS: RAT reliably detect SARS-CoV-2 from symptomatic adults in the community presenting within 7 days of symptom onset using nasal swabs or nasalNP. High agreement with rPan can avoid the need for collecting a second swab for RT-PCR confirmation or testing of variants of concern from positive RAT in this population.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".