Detection of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in outpatients: A multicenter comparison of self-collected saline gargle, oral swab, and combined oral–anterior nasal swab to a provider collected nasopharyngeal swab
Bibliographic record
Abstract
BACKGROUND: Widespread testing for severe acute respiratory coronavirus virus 2 (SARS-CoV-2) is necessary to curb the spread of coronavirus disease 2019 (COVID-19), but testing is undermined when the only option is a nasopharyngeal swab. Self-collected swab techniques can overcome many of the disadvantages of a nasopharyngeal swab, but they require evaluation. METHODS: Three self-collected non-nasopharyngeal swab techniques (saline gargle, oral swab and combined oral-anterior nasal swab) were compared to a nasopharyngeal swab for SARS-CoV-2 detection at multiple COVID-19 assessment centers in Toronto, Canada. The performance characteristics of each test were assessed. RESULTS: The adjusted sensitivity of the saline gargle was 0.90 (95% CI 0.86-0.94), the oral swab was 0.82 (95% CI, 0.72-0.89) and the combined oral-anterior nasal swab was 0.87 (95% CI, 0.77-0.93) compared to a nasopharyngeal swab, which demonstrated a sensitivity of ˜90% when all positive tests were the reference standard. The median cycle threshold values for the SARS-CoV-2 E-gene for concordant and discordant saline gargle specimens were 17 and 31 (P < .001), for the oral swabs these values were 17 and 28 (P < .001), and for oral-anterior nasal swabs these values were 18 and 31 (P = .007). CONCLUSIONS: Self-collected saline gargle and an oral-anterior nasal swab have a similar sensitivity to a nasopharyngeal swab for the detection of SARS-CoV-2. These alternative collection techniques are cheap and can eliminate barriers to testing, particularly in underserved populations.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".