Serological Evaluation of Human Antibodies of the Immunoglobulin Class A and G Against SARS-CoV-2 in Serum Collected in Newfoundland and Labrador
Bibliographic record
Abstract
The ability to detect antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently under investigation with various performance characteristics and indications for use. In this article, we analyzed the ability of the Abbott SARS-CoV-2 immunoglobulin class G (IgG), EuroImmun SARS-CoV-2 enzyme-linked immunosorbent assay (ELISA) IgG, and EuroImmun SARS-CoV-2 ELISA immunoglobulin class A (IgA) kits to detect evidence of previous infection with SARS-CoV-2. We tested 49 known coronavirus disease-19 (COVID-19) patients and 111 prepandemic stored serology specimens. This resulted in a sensitivity of 95.9%, 100.0%, and 91.3% and a specificity of 98.2%, 98.2%, and 90.8% respectively, using manufacturer recommended cutoffs after inconclusive results (one for EuroImmun IgG and five for EuroImmun IgA) being excluded in the final statistical analyses. Cross-reactivity of hepatitis C virus seropositive specimens was observed resulting in false positives (p < 0.05). If a two-tiered algorithmic approach was applied, that is, testing with Abbott SARS-CoV-2 assay followed by EuroImmun SARS-CoV-2 IgG, 100% specificity and sensitivity could be obtained after six inconclusive results were excluded from data set before statistical analyses. Performance characteristics presented demonstrate the superior performance of IgG class antibodies for investigating previous infections. In addition, utilizing a second antibody test for supplementary testing may significantly enhance performance, particularly in lower prevalence settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".