Calibration and Evaluation of Quantitative Antibody Titers for Varicella-Zoster Virus by Use of the BioPlex 2200
Bibliographic record
Abstract
Most commercially available enzyme immunoassay-based methods have limited sensitivity to detect antibody responses to varicella-zoster virus (VZV) in vaccinated individuals, who produce lower antibody levels than those with natural infection. However, more sensitive methods are either not commercially available or less amenable to high-throughput testing. The BioPlex 2200 measles, mumps, rubella, and varicella (MMRV) IgG assay (Bio-Rad Laboratories, Hercules, CA) is an automated high-throughput platform based on the microsphere Luminex technology that measures antibodies against measles, mumps, rubella, and varicella viruses simultaneously. Although it has U.S. Food and Drug Administration approval as a qualitative diagnostic test for measles, mumps, rubella, and varicella virus immunity, in this study, we have validated the assay to produce quantitative titers (off label) against the VaccZyme VZV glycoprotein (VZVgp) low-level IgG kit (The Binding Site Ltd., Birmingham, UK) using the World Health Organization international standard. Here, we show that the BioPlex 2200 MMRV IgG assay has sensitivity superior to that of the Zeus enzyme-linked immunosorbent assay (ELISA) VZV IgG assay (Zeus Diagnostics, Branchburg, NJ). Using receiver operating characteristic (ROC) analysis and adjusting the cutoff levels, we improved the sensitivity of the quantitative BioPlex 2200 MMRV IgG assay to 97.4%, while maintaining 100% specificity.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".