Comparison analysis of different swabs and transport mediums suitable for SARS-CoV-2 testing following shortages
Bibliographic record
Abstract
On March 11, 2020, the World Health Organization (WHO) assessed COVID-19, caused by SARS-CoV-2, as a pandemic. As of June 1, 2020, SARS-CoV-2 has had a documented effect of over 6 million cases world-wide, amounting to over 370,000 deaths (World Health Organization, 2020. Novel Coronavirus (COVID-19) Situation. http://https://covid19.who.int/). Consequently, the high demand for testing has resulted in a depletion of commercially available consumables, including the recommended swabs and viral transport media (VTM) required for nasopharyngeal sampling. Therefore, the potential use of unvalidated alternatives must be explored to address the global shortage of testing supplies. To tackle this issue, we evaluated the utility of different swabs and transport mediums for the molecular detection of SARS-CoV-2. This study compared the performance of six swabs commonly found in primary and tertiary health care settings (PurFlock Ultra, FLOQSwab, Puritan Pur-Wraps cotton tipped applicators, Puritan polyester tipped applicators, MedPro 6" cotton tipped applicators, and HOLOGIC Aptima) for their efficacy in testing for SARS-CoV-2. Separately, the molecular detection of SARS-CoV-2 was completed from different transport mediums (DMEM, PBS, 100 % ethanol, 0.9 % normal saline and VTM), which were kept up to three days at room temperature (RT). The results indicate that there is no meaningful difference in viral yield from different swabs and most transport mediums for the collection and detection of SARS-CoV-2, indicating swab and medium alternatives could be used if supplies run out.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".