Food Allergy: From Clinical Presentation to Management and Prevention
Bibliographic record
Abstract
Food allergy is an adverse immune-mediated pathological reaction directed toward proteins or glycoproteins in food. It affects the skin, gastrointestinal, respiratory and cardiovascular systems, resulting in a broad spectrum of diverse clinical presentations. Consequently, establishing a diagnosis can present a great challenge. The prevalence rate of food allergy is increasing, particularly in modern industrialized countries, and is becoming a significant public health problem. There is still no current treatment, and avoidance of suspected food allergens remains the most important treatment modality. However, in order to avoid unnecessary dietary restrictions, food hypersensitivity should be confirmed using allergy tests prior to introduction of elimination diet. In cases of validation of food allergy, avoiding suspected foods are recommended. Education of patients is the cornerstone of prevention and therapy; providing all relevant information on how to exclude specific foods from the patient's diet, as well as how to detect and manage allergic reactions, especially in severe cases like anaphylaxis. This review aims at presenting the clinical picture and diagnosis, as well as discussing current treatment and preventive strategies for different types of food allergies.
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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".