COVID-19-vaccination in patients receiving allergen immunotherapy (AIT) or biologics - EAACI recommendations
Bibliographic record
Abstract
Immune modulation is a key therapeutic tool for allergic diseases and asthma. It can be achieved in an antigen-specific way via allergen immunotherapy (AIT) or in endotype-driven approach using biologicals that target the major pathways of the type 2 (T2) immune response: IgE, IL-5 and IL-4/IL-13. COVID-19 vaccine provides an excellent opportunity to tackle the global pandemics and is currently being applied in an accelerated rhythm worldwide. It works as well through immune modulation. Thus, as there is an obvious interference between these treatment modalities recommendations on how they should be applied in sequence are expected. The European Academy of Allergy and Clinical Immunology (EAACI) gathered an outstanding expert panel under its Research and Outreach Committee (ROC). This expert panel was called to evaluate the evidence and formulate recommendation on the administration of COVID-19 vaccine in patients with allergic diseases and asthma receiving AIT or biologicals. The panel also formulated recommendations for COVID-19 vaccine in association with biologicals targeting the type 1 or type 3 immune response. In formulating recommendations, the panel evaluated the mechanisms of COVID-19 infection, of COVID-19 vaccine, of AIT and of biologicals and considered the data published for other anti-infectious vaccines administered concurrently with AIT or biologicals.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".