Bibliographic record
Abstract
Food allergy (FA) is a significant public health concern, with its prevalence rising globally and greatly affecting the lives of patients and their families. The increasing burden on healthcare systems and the impact on quality of life underscore the need for better understanding and management strategies. The dual-allergen hypothesis suggests that early skin exposure to allergens increases sensitization risk, while early oral exposure and sustained ingestion of foods promote tolerance While diet is not the only factor in FA development, eating allergenic foods early and often can profoundly prevent FA despite other risk factors such as eczema. Treatment approaches vary by a number of factors including patient preference. Avoidance remains an option, but tailored avoidance, such as allowing denatured food products with milk and egg is now commonplace. Food immunotherapy approaches via multiple routes and doses are becoming more available. Immunotherapy can result in marked reductions in food reactivity which may be sustained for weeks or months or even longer off treatment for some patients. Biologics are also being more widely offered to increase the amount of food that can be safely ingested to facilitate immunotherapy. With the current approaches, treatment at a time of low IgE formation, often associated with younger age, may be the most effective for remission, but older ages may benefit from the increase in the threshold of reactivity that food-based treatments can provide.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 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".