Evaluation of SARS-CoV-2 interferon gamma release assay in BNT162b2 vaccinated healthcare workers
Bibliographic record
Abstract
To predict protective immunity to SARS-CoV-2, cellular immunity seems to be more sensitive than humoral immunity. Through an Interferon-Gamma (IFN-γ) Release Assay (IGRA), we show that, despite a marked decrease in total antibodies, 94.3% of 123 healthcare workers have a positive cellular response 6 months after inoculation with the 2nd dose of BNT162b2 vaccine. Despite the qualitative relationship found, we did not observe a quantitative correlation between IFN-γ and IgG levels against SARS-CoV-2. Using stimulated whole blood from a subset of participants, we confirmed the specific T-cell response to SARS-CoV-2 by dosing elevated levels of the IL-6, IL-10 and TNF-α. Through a 20-month follow-up, we found that none of the infected participants had severe COVID-19 and that the first positive cases were only 12 months after the 2nd dose inoculation. Future studies are needed to understand if IGRA-SARS-CoV-2 can be a powerful diagnostic tool to predict future COVID-19 severe disease, guiding vaccination policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".