Management of IgE‐mediated food allergy in the 21st century
Bibliographic record
Abstract
The 21st century has seen the propulsion of research in the field of food allergy, which has driven real changes in the clinical approach. Allergen immunotherapy has been recommended for the active management of food allergy. Data have shown promising additional methods of treatment, including biologics. Efforts have been devoted to the risk stratification of food allergy and the standardization of the assessment of food-allergic severity. Alternative routes of administration of epinephrine are under investigation to minimize any mechanical issue and the fear of injections. Evidence-based guidelines have been published by the main international societies in the field of anaphylaxis and food allergy management and new updates are in preparation. In the coming years, treatment options that are currently in pre-clinical or early clinical evaluation will hopefully lead to safe and effective disease-modifying therapies for food allergy in clinical practice. The identification of reliable biomarkers and the standardization of definitions and measurement approaches, alongside a shared decision-making with patients and families, will be key for the development of personalized care and to help minimize the substantial burden of food allergy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".