Bibliographic record
Abstract
David Yue, Amanda Ciccolini, Ernie Avilla, Susan Waserman Division of Clinical Immunology & Allergy, Department of Medicine, Health Sciences Centre, McMaster University, Hamilton, ON, Canada Abstract: Anaphylaxis is a severe and potentially life-threatening allergic reaction. There are numerous potential causes, with food allergy being the leading cause in children and the focus of this review. Most reactions involve an IgE-mediated mechanism, although non-IgE-mediated and nonimmunologic reactions can occur. Various cofactors to be discussed can place certain individuals at an increased risk of severe or fatal anaphylaxis. The clinical manifestations of anaphylaxis are broad and may involve multiple body systems. Diagnosis of food-related anaphylaxis is primarily based on signs and symptoms and supported, wherever possible, by identification and confirmation of a culprit food allergen. First-line treatment of anaphylaxis is intramuscular administration of epinephrine. Long-term management is generally focused on strict allergen avoidance and more recently on food desensitization using immunotherapy. This review provides an overview of anaphylaxis with a specific focus on food allergy. Keywords: allergic reaction, food, trigger, epinephrine, avoidance, immunotherapy
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.016 | 0.007 |
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".