Clinical Performance of Self-Collected Nasal Swabs and Antigen Rapid Tests for SARS-CoV-2 Detection in Resource-Poor Settings
Bibliographic record
Abstract
Background: In resource-poor countries, antigen-based rapid tests (Ag-RDTs) performed at primary healthcare and community settings improved access to SARS-CoV-2 diagnostics. However, the technical skills and biosafety requirements inherent to nasopharyngeal and oropharyngeal (OP) specimens limit the scale-up of SARS-CoV-2 testing. The collection of nasal-swabs is programmatically viable, but its performance has not been evaluated in resource-poor settings. Methods: We first evaluated the performance of SteriPack self-collected nasal swabs for the detection of SARS-CoV-2 by real-time PCR in 1498 consecutively enrolled patients with suspected infection. Next, we evaluated the clinical performance of three nasal swab-based Ag-RDTs against real-time PCR on OP specimens. Results: The sensitivity of nasal swabs was 80.6% [95% CI: 75.3−85.2%] compared to OP specimens. There was a good correlation (r = 0.58; p < 0.0001) between Ct values of 213 positive cases obtained using nasal and OP swabs. Our findings show sensitivities of 79.7% [95% CI: 73.3−85.1%] for Panbio COVID-19 Ag-RDT, 59.6% [95% CI: 55.2−63.8%] for COVIOS Ag-RDT, and 78.0% [95% CI: 73.5−82.0%] for the LumiraDx SARS-CoV-2 Ag-RDT. Conclusions: In our setting, the COVIOS Ag-RDT did not meet WHO requirements. Nasal swab-based Ag-RDTs for SARS-CoV-2 detection constitute a viable and accurate diagnostic option in resource-poor settings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".