<scp>SARS‐CoV‐2</scp> immunoassays in a predominantly vaccinated population: Performances and qualitative agreements obtained with two analytical approaches and four immunoassays
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serosurveys are typically analysed by applying a fixed threshold for seropositivity ('conventional approach'). However, this approach underestimates the seroprevalence of anti-nucleocapsid (N) in vaccinated individuals-who often exhibit a difficult-to-detect anti-N response. This limitation is compounded by delays between the onset of infection and sample collection. To address this issue, we compared the performance of four immunoassays using a new analytical approach ('ratio-based approach'), which determines seropositivity based on an increase in anti-N levels. MATERIALS AND METHODS: Two groups of plasma donors and four immunoassays (Elecsys total anti-N, VITROS total anti-N, Architect anti-N Immunoglobulin G (IgG) and in-house total anti-N) were evaluated. First-group donors (N = 145) had one positive SARS-CoV-2 polymerase chain reaction (PCR) test result and had made two plasma donations, including one before and one after the PCR test (median = 27 days post-PCR). Second-group donors (N = 100) had made two plasma donations early in the Omicron wave. RESULTS: Among first-group donors (97.9% vaccinated), sensitivity estimates ranged from 60.0% to 89.0% with the conventional approach, compared with 94.5% to 98.6% with the ratio-based approach. Among second-group donors, Fleiss's κ ranged from 0.56 to 0.83 with the conventional approach, compared with 0.90 to 1.00 with the ratio-based approach. CONCLUSION: With the conventional approach, the sensitivity of four immunoassays-measured in a predominantly vaccinated population based on samples collected ~1 month after a positive test result-fell below regulatory agencies requirement of ≥95%. The ratio-based approach significantly improved the sensitivities and qualitative agreement among immunoassays, to the point where all would meet this requirement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".