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
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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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it