Management of Food-Induced Anaphylaxis: Unsolved Challenges
Bibliographic record
Abstract
Anaphylaxis is an acute and often severe systemic allergic reaction. The prevalence of food allergy has been increasing and is currently estimated at approximately 3.5%. Food allergic reactions account for one-third to one-half of anaphylaxis cases worldwide. It is estimated that approximately 30,000 food-related anaphylactic reactions are treated in United States emergency departments (ED) every year resulting in approximately 2000 hospitalizations and 150 deaths. The increasing rate of food-induced anaphylactic episodes in the last few decades underlines the existence of major challenges. This review will critically appraise current guidelines for the diagnosis as well as the acute and long-term management of food-induced anaphylaxis (FIA). Importantly, it will outline existing challenges and suggest measures to improve outcomes in patients with FIA. We propose that the discovery of novel diagnostic (i.e. biomarkers and predictors) and therapeutic approaches is a major challenge that may be overcome as the mechanisms underlying FIA are better delineated. We further propose that better dissemination, implementation and compliance with the consensus management guidelines are urgently needed. This will require education of ED personnel, patient empowerment as well as effective multilateral communication among patients, emergency and family physicians, allergists and specialized volunteer organizations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".