AllergoOncology: ultra-low IgE, a potential novel biomarker in cancer—a Position Paper of the European Academy of Allergy and Clinical Immunology (EAACI)
Bibliographic record
Abstract
Elevated serum IgE levels are associated with allergic disorders, parasitosis and specific immunologic abnormalities. In addition, epidemiological and mechanistic evidence indicates an association between IgE-mediated immune surveillance and protection from tumour growth. Intriguingly, recent studies reveal a correlation between IgE deficiency and increased malignancy risk. This is the first review discussing IgE levels and links to pathological conditions, with special focus on the potential clinical significance of ultra-low serum IgE levels and risk of malignancy. In this Position Paper we discuss: (a) the utility of measuring total IgE levels in the management of allergies, parasitosis, and immunodeficiencies, (b) factors that may influence serum IgE levels, (c) IgE as a marker of different disorders, and d) the relationship between ultra-low IgE levels and malignancy susceptibility. While elevated serum IgE is generally associated with allergic/atopic conditions, very low or absent IgE may hamper anti-tumour surveillance, indicating the importance of a balanced IgE-mediated immune function. Ultra-low IgE may prove to be an unexpected biomarker for cancer risk. Nevertheless, given the early stage of investigations conducted mostly in patients with diseases that influence IgE levels, in-depth mechanistic studies and stratification of malignancy risk based on associated demographic, immunological and clinical co-factors are warranted.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".