Magnitude of venous or capillary blood-derived SARS-CoV-2-specific T cell response determines COVID-19 immunity
Bibliographic record
Abstract
T cells specific for SARS-CoV-2 are thought to protect against infection and development of COVID-19, but direct evidence for this is lacking. Here, we associated whole-blood-based measurement of SARS-CoV-2-specific interferon-γ-positive T cell responses with positive COVID-19 diagnostic (PCR and/or lateral flow) test results up to 6 months post-blood sampling. Amongst 148 participants donating venous blood samples, SARS-CoV-2-specific T cell response magnitude is significantly greater in those who remain protected versus those who become infected (P < 0.0001); relatively low magnitude T cell response results in a 43.2% risk of infection, whereas high magnitude reduces this risk to 5.4%. These findings are recapitulated in a further 299 participants testing a scalable capillary blood-based assay that could facilitate the acquisition of population-scale T cell immunity data (14.9% and 4.4%, respectively). Hence, measurement of SARS-CoV-2-specific T cells can prognosticate infection risk and should be assessed when monitoring individual and population immunity status.
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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".