Combined oropharyngeal/nares and nasopharyngeal swab sampling remain effective for molecular detection of SARS-CoV-2 Omicron variant
Bibliographic record
Abstract
The world has experienced several waves of SARS-CoV-2 variants of concern (VoCs) throughout the COVID-19 pandemic since the first cases in December 2019. The Omicron VoC has increased transmission, compared to its predecessors, and can present with sore throat and other cold-like symptoms. Given the predominance of throat symptoms, and previous work demonstrating better sensitivity using antigen-based rapid detection tests when a throat swab is included in the standard nasal sampling, this quality improvement project sought to ensure ongoing suitability of both combined oropharyngeal/nares (OPN) and nasopharyngeal (NP) swab sampling used throughout the pandemic. Consenting participants meeting Public Health testing criteria (mostly symptomatic or a close contact of a known case) were enrolled, and paired NP and OPN swabs were subjected to nucleic acid amplification testing (NAAT). Comparing paired specimens from 392 participants sensitivity of NP swabs was 89.1 % (95 % CI, 78.8–94.9), and that of OPN was 98.4 % (95 % CI: 90.9->99.9) ( P -value 0.052). This project demonstrated that both NP and combined OPN swabs detected the Omicron variant with similar sensitivity by NAAT, supporting the continued use of either swab collection for SARS-CoV-2 molecular detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".