Comparison of six COVID-19 serology assays for detection of antibodies from patients infected with ancestral and a spectrum of SARS-CoV-2 variants
Bibliographic record
Abstract
Serology assays against spike, receptor binding domain (RBD) and nucleocapsid proteins of the severe acute respiratory syndrome coronavirus 2 are essential for serosurveillance. We performed a comparison of four medium-to-high throughput commercial assays [Abbott Laboratories, Ortho Clinical Diagnostics, Meso Scale Diagnostics (MSD)], one point-of-care test (ZEKMED) and a laboratory-developed plaque reduction neutralization test using a reference panel and clinical specimens. Overall, the assays showed a high positive percent agreement of ≥85% and negative percent agreement of ≥90%, with the MSD anti-spike IgG assay having the best performance (100% in both). Notably, Abbott anti-nucleocapsid IgG, MSD anti-spike IgG and ZEKMED anti-spike RBD IgM/IgG combined assays were able to detect antibodies from individuals infected with all different variants tested – Alpha, Beta, Gamma, Delta and Omicron. The limit of detection (LOD) of the assays ranged from 9.9 to 62.0 BAU ml−1, with the Abbott anti-spike RBD having the lowest LOD. The COVID-19 serology assays will continue to be useful in determining seroprevalence from infection and vaccination.
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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".