Natural spring water gargle samples as an alternative to nasopharyngeal swabs for SARS‐CoV‐2 detection using a laboratory‐developed test
Bibliographic record
Abstract
Abstract The objective of this study was to validate the use of spring water gargle (SWG) as an alternative to oral and nasopharyngeal swab (ONPS) for SARS‐CoV‐2 detection with a laboratory‐developed test. Healthcare workers and adults from the general population, presenting to one of two COVID‐19 screening clinics in Montréal and Québec City, were prospectively recruited to provide a gargle sample in addition to the standard ONPS. The paired specimens were analyzed using thermal lysis followed by a laboratory‐developed nucleic acid amplification test (LD‐NAAT) to detect SARS‐CoV‐2, and comparative performance analysis was performed. An individual was considered infected if a positive result was obtained on either sample. A total of 1297 adult participants were recruited. Invalid results (n = 18) were excluded from the analysis. SARS‐CoV‐2 was detected in 144/1279 (11.3%) participants: 126 from both samples, 15 only from ONPS, and 3 only from SWG. Overall, the sensitivity was 97.9% (95% CI: 93.7–99.3) for ONPS and 89.6% (95% CI: 83.4–93.6; p = 0.005) for SWG. The mean ONPS cycle threshold (Ct) value was significantly lower for the concordant paired samples as compared to discordant ones (22.9 vs. 32.1; p < 0.001). In conclusion, using an LD‐NAAT with thermal lysis, SWG is a less sensitive sampling method than the ONPS. However, the higher acceptability of SWG might enable a higher rate of detection from a population‐based perspective. Nonetheless, in patients with a high clinical suspicion of COVID‐19, a repeated analysis with ONPS should be considered. The sensitivity of SWG using NAAT preceded by chemical extraction should be evaluated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".