Immunologically relevant aspects of the new COVID-19 vaccines—an ÖGAI (Austrian Society for Allergology and Immunology) and AeDA (German Society for Applied Allergology) position paper
Bibliographic record
Abstract
BACKGROUND: The vaccines against the coronavirus disease 2019 (COVID-19) approved in the European Union represent a decisive step in the fight against the pandemic. The application of these available vaccines to patients with pre-existing immunological conditions leads to a multitude of questions regarding efficacy, side effects and the necessary patient information. RESULTS: This review article provides insight into mechanisms of action of the currently available severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines and summarises the current state of science as well as expert recommendations regarding tolerability of the vaccines. In addition, the potential to develop protective immune responses is determined. A special focus is given on patients under immunosuppression or in treatment with immunomodulatory drugs. Special groups of the population such as children, pregnant women and the elderly are also considered. CONCLUSION: Despite the need for a patient-specific risk-benefit assessment, the consensus among experts is that patients with immunological diseases in particular benefit from the induced immune protection after COVID-19 vaccination and do not have an increased risk of side effects.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".