Evaluation of commercial SARS-CoV-2 serological assays in Canadian public health laboratories
Bibliographic record
Abstract
The COVID-19 pandemic has led to the influx of immunoassays for the detection of antibodies towards severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into the global market. The Canadian Public Health Laboratory Network Serology Task Force undertook a nationwide evaluation of twelve laboratory and 6 point-of-care based commercial serological assays for the detection of SARS-CoV-2 antibodies. We determined that there was considerable variability in the performance of individual tests and that an orthogonal testing algorithm should be prioritized to maximize the accuracy and comparability of results across the country. The manual enzyme immunoassays and point-of-care tests evaluated had lower specificity and increased coefficients of variation compared to automated enzyme immunoassays platforms putting into question their utility for large-scale sero-surveillance. Overall, the data presented here provide a comprehensive approach for applying accurate serological assays for longitudinal sero-surveillance and vaccine trials while informing Canadian public health policy.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".