Evaluation of anti-nucleocapsid level variation to assess SARS-CoV-2 seroprevalence in a vaccinated population
Bibliographic record
Abstract
BACKGROUND: Serosurveys have been key to public health decision-making since the beginning of the SARS-CoV-2 pandemic. However, several studies have uncovered that vaccination blunts the anti-nucleocapsid (N) response to a subsequent infection, which hinders the ability of serologic assays (including commercial ones) to detect recent infections. We therefore developed a new analytical approach to increase the sensitivity of detection of infection in vaccinated individuals. METHODS: Two samples were obtained from 248 SARS-CoV-2-positive (PCR-confirmed), vaccinated donors: one before the infection (reference sample) and one after (test sample). All samples were tested using an in-house, anti-N enzyme-linked immunosorbent assay (ELISA) which had a sensitivity of 98.1% before the mass vaccination campaign. Instead of applying a seropositivity threshold based on a single absorbance value (i.e. conventional approach), seropositivity was determined based on the ratio between the anti-N absorbance of the test and reference samples. RESULTS: The sensitivity of the new approach to detect infection in vaccinated individuals was 95.2% using a cut-off of 1.5 for the anti-N ratio, whereas that of the conventional approach was 63.3%. CONCLUSION: The new analytical approach described herein captured a significantly greater proportion of vaccinated individuals with a known history of SARS-CoV-2 infection than the conventional approach used in most serosurveys.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".