Dried blood spot specimens for SARS-CoV-2 antibody testing: A multi-site, multi-assay comparison
Bibliographic record
Abstract
The true severity of infection due to COVID-19 is under-represented because it is based on only those who are tested. Although nucleic acid amplifications tests (NAAT) are the gold standard for COVID-19 diagnostic testing, serological assays provide better population-level SARS-CoV-2 prevalence estimates. Implementing large sero-surveys present several logistical challenges within Canada due its unique geography including rural and remote communities. Dried blood spot (DBS) sampling is a practical solution but comparative performance data on SARS-CoV-2 serological tests using DBS is currently lacking. Here we present test performance data from a well-characterized SARS-CoV-2 DBS panel sent to laboratories across Canada representing 10 commercial and 2 in-house developed tests for SARS-CoV-2 antibodies. Three commercial assays identified all positive and negative DBS correctly corresponding to a sensitivity, specificity, positive predictive value, and negative predictive value of 100% (95% CI = 72.2, 100). Two in-house assays also performed equally well. In contrast, several commercial assays could not achieve a sensitivity greater than 40% or a negative predictive value greater than 60%. Our findings represent the foundation for future validation studies on DBS specimens that will play a central role in strengthening Canada's public health policy in response to COVID-19.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".