AllergoOncology – the impact of allergy in oncology: <scp>EAACI</scp> position paper
Bibliographic record
Abstract
Th2 immunity and allergic immune surveillance play critical roles in host responses to pathogens, parasites and allergens. Numerous studies have reported significant links between Th2 responses and cancer, including insights into the functions of IgE antibodies and associated effector cells in both antitumour immune surveillance and therapy. The interdisciplinary field of AllergoOncology was given Task Force status by the European Academy of Allergy and Clinical Immunology in 2014. Affiliated expert groups focus on the interface between allergic responses and cancer, applied to immune surveillance, immunomodulation and the functions of IgE-mediated immune responses against cancer, to derive novel insights into more effective treatments. Coincident with rapid expansion in clinical application of cancer immunotherapies, here we review the current state-of-the-art and future translational opportunities, as well as challenges in this relatively new field. Recent developments include improved understanding of Th2 antibodies, intratumoral innate allergy effector cells and mediators, IgE-mediated tumour antigen cross-presentation by dendritic cells, as well as immunotherapeutic strategies such as vaccines and recombinant antibodies, and finally, the management of allergy in daily clinical oncology. Shedding light on the crosstalk between allergic response and cancer is paving the way for new avenues of treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".