Enabling the evaluation of COVID-19 vaccines with correlates of protection
Bibliographic record
Abstract
In February 2023, a meeting about correlates of protection (CoPs) against COVID-19 was organized by the International Alliance for Biological Standardization, the European Plotkin Institute for Vaccinology, and Vaccinopolis. The meeting aimed at reviewing the evidence, drawing conclusions, and identifying knowledge gaps. Collection of evidence is not straightforward. Neutralizing antibodies correlate with protection and are used for immunobridging studies within and between vaccine platforms for approval of new COVID-19 vaccines. In preparation for the next pandemic, it is vital that rapidly authorized initial vaccines are available to perform immunobridging studies very early. Additional components of the immune response likely contribute to protection against symptomatic infection. Current evidence is strongest for T lymphocytes and binding antibodies. Further studies are needed to consolidate this evidence and define their potential role in the evaluation of vaccines. For evaluation of mucosal vaccines, identifying CoPs against infection and transmission is key; further research is needed to identify and standardize methods suitable for clinical studies. CoPs for broadly protective beta-coronavirus vaccines remain a critical area of research. The knowledge, expertise, and capacity exist to conduct clinical studies using different designs in different populations to discover and validate CoPs, facilitating and accelerating evaluation of novel vaccines/vaccination platforms.
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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 | 0.076 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".