Comparison of the analytical sensitivity of COVID-19 rapid antigen tests in Australia and Canada
Bibliographic record
Abstract
Rapid testing has become an indispensable strategy to identify the most infectious individuals and prevent the transmission of SARS-CoV-2 in vulnerable populations. As such, COVID-19 rapid antigen tests (RATs) are being manufactured faster than ever yet lack relevant comparative analyses required to inform on absolute analytical sensitivity and performance, limiting end-user ability to accurately compare brands for decision making. To date, more than 1000 different COVID-19 RATs are commercially available in the world, most of which detect the viral nucleocapsid protein (NP). Here, we examine and compare the analytical sensitivity of 26 RATs that are readily available in Canada and/or Australia using two NP reference materials (RMs) – a fluorescent NP-GFP expressed in bacterial cells and NCAP-1 produced in a mammalian expression system. Both RMs generate highly comparable results within each RAT, indicating minimal bias due to differing expression systems and final buffer compositions. However, we demonstrate orders of magnitude differences in analytical sensitivities among distinct RATs, and find little correlation with the median tissue culture infectious dose (TCID50) assay values reported by manufacturers. In addition, two COVID-19/Influenza A&B combination RATs were evaluated with influenza A NP-GFP. Finally, important logistics considerations are discussed regarding the robustness, ease of international shipping and safe use of these reference proteins. Taken together, our data highlight the need for and practicality of readily available, reliable reference proteins for end-users that will ensure that manufacturers maintain batch-to-batch quality and accuracy of RATs. They will aid international public health and government agencies, as well as health and aged care facilities to reliably benchmark and select the best RATs to curb transmission of future SARS-CoV-2 and influenza outbreaks.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".