Self-Collection of Nose and Throat Swab Samples for SARS-CoV-2 Antigen Testing
Bibliographic record
Abstract
The emergence of new variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised questions about the accuracy of currently available rapid antigen detection tests (RADTs) for the detection of these variants using the currently authorized sampling methods. Six Canadian provinces and 2 international jurisdictions have formally recommended people swab both their throat and nose when performing a RADT in response to the emergence of the Omicron variant. The evidence used to support these decisions was not clearly reported. Three pre-print non-peer reviewed publications (1 from Canada, 2 from the Netherlands) were identified regarding the diagnostic accuracy and clinical utility of RADTs using dual nasal and throat self-collected sample for suspected COVID-19. No relevant studies were identified that included children younger than 16 years old. Findings from these publications indicate that using self-collected combined nasal plus throat samples, instead of self-collected nasal samples for RADTs, resulted in greater detection rates without impacting true negative rates. Furthermore, combined nasal plus throat sampling is associated with high participant acceptability and tolerability, ease of use, and low incidence of harms (when reported). However, the limitations of these publications (e.g., non-peer reviewed pre-prints; dual nasal and throat sampling with swabs approved for nasal sampling only) should be taken into consideration when interpreting these findings. As new variants emerge and real-world clinical evaluations are published, regulatory bodies and/or RADT manufacturers could consider reassessing sampling methods, suitable swab types, and testing instructions.
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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 | 0.038 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".