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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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