Comparative performance data for multiplex SARS-CoV-2 serological assays from a large panel of dried blood spot specimens
Bibliographic record
Abstract
The extent of the COVID-19 pandemic will be better understood through serosurveys and SARS-CoV-2 antibody testing. Dried blood spot (DBS) samples will play a central role in large scale serosurveillance by simplifying biological specimen collection and transportation, especially in Canada. Direct comparative performance data on multiplex SARS-CoV-2 assays resulting from identical DBS samples are currently lacking. In our study, we aimed to provide performance data for the BioPlex 2200 SARS-CoV-2 IgG (Bio-Rad), V-PLEX SARS-CoV-2 Panel 2 IgG (MSD), and Elecsys Anti-SARS-CoV-2 (Roche) commercial assays, as well as for two highly scalable in-house assays (University of Ottawa and Mount Sinai Hospital protocols) to assess their suitability for DBS-based SARS-CoV-2 DBS serosurveillance. These assays were evaluated against identical panels of DBS samples collected from convalescent COVID-19 patients ( n = 97) and individuals undergoing routine sexually transmitted and bloodborne infection (STBBI) testing prior to the COVID-19 pandemic ( n = 90). Our findings suggest that several assays are suitable for serosurveillance (sensitivity >97% and specificity >98%). In contrast to other reports, we did not observe an improvement in performance using multiple antigen consensus-based rules to establish overall seropositivity. This may be due to our DBS panel which consisted of samples collected from convalescent COVID-19 patients with significant anti-spike, -receptor binding domain (RBD), and -nucleocapsid antibody titers. This study demonstrates that biological specimens collected as DBS coupled with one of several readily available assays are useful for large-scale COVID-19 serosurveillance.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".