Evaluation of Three Anti-SARS-CoV-2 Serologic Immunoassays for Post-Vaccine Response
Bibliographic record
Abstract
BACKGROUND: In North America, both messenger RNA (mRNA) vaccines, Pfizer-BioNTech BNT162b2, and Moderna mRNA-1273, each utilizing a 2-dose regimen, have started to be administered to individuals. METHODS: We evaluated the quantitative serologic antibody response following administration of either a single dose or both doses of an mRNA severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine in a cohort of 98 participants (88 healthcare workers [HCW] and 10 solid organ transplant [SOT] recipients). Antibody levels were compared across 3 immunoassays: Elecsys Anti-SARS-CoV-2 S (Roche Diagnostics), SARS-CoV-2 TrimericS IgG (DiaSorin), and SARS-CoV-2 IgG II Quant (Abbott). RESULTS: Among HCW, sensitivity ranged from 100% (Roche), 99% (Abbott) and 98% (DiaSorin). The SARS-CoV-2 IgG II Quant and SARS-CoV-2 TrimericS IgG assays showed good agreement with a Pearson correlation coefficient of R = 0.95. Pearson correlation coefficients of R = 0.82 and 0.83 were obtained for Elecsys Anti-SARS-CoV-2 S vs SARS-CoV-2 TrimericS IgG and SARS-CoV-2 IgG II Quant vs Elecsys Anti-SARS-CoV-2 S, respectively. Significant differences in antibody levels between HCW and SOT recipients were observed. A decrease in antibody levels from time of vaccine administration to blood draw was evident. Among those with a second dose, an increase in antibody levels with increased time between administration of the first and second dose was observed. CONCLUSIONS: The absolute values generated from each of the assay platforms are not interchangeable. Antibody levels differed with increased time between vaccine administration and with increased time between administration of the first and second dose. Further, significant differences in antibody levels between HCW and SOT recipients were observed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".