Detection of anti-nucleocapsid antibodies associated with primary SARS-CoV-2 infection in unvaccinated and vaccinated blood donors
Bibliographic record
Abstract
Abstract Anti-nucleocapsid (N) antibody assays can be used to estimate SARS-CoV-2 infection prevalence in regions employing anti-spike based COVID-19 vaccines. However, poor sensitivity of anti-N assays in detecting infections after vaccination (VI) has been reported. To support serological monitoring of infections, including VI, in a large blood donor cohort (N=142,599), we derived a lower cutoff for identifying previous infection using the Ortho VITROS Anti-SARS-CoV-2 Total-N Antibody assay, improving sensitivity while maintaining specificity >98%. Sensitivity was validated in samples donated after self-reported infections diagnosed by a swab-based test. Sensitivity for first infections in unvaccinated donors was 98.1% (95% CI: 98.0,98.2) and for VI was 95.6% (95.6,95.7), using the standard cutoff. Regression analysis showed sensitivity was reduced in the Delta compared to Omicron period, in older donors, asymptomatic infections, ≤30 days after infection and for VI. The standard Ortho anti-N threshold demonstrated good sensitivity, which was modestly improved with the revised cutoff.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".