Markers of tolerance development to food allergens
Bibliographic record
Abstract
IgE-mediated reactions to food allergens are the most common cause of anaphylaxis in childhood. Although allergies to cow's milk, egg, or soy proteins, in contrast to peanut and tree nut allergens, resolve within the first 6 years of life in up to 60% due to natural tolerance development, this process is not well understood. At present, there is no cure or treatment for food allergy that would result in an induction of tolerance to the symptom-eliciting food. Avoidance, providing an emergency plan and education, is the standard of treatment. Oral immunotherapeutic approaches have been proven reasonable efficacy; however, they are associated with high rates of side-effects and low numbers of patients achieving tolerance. Nevertheless, mechanisms that take place during oral immunotherapy may help to understand tolerance development. On the basis of these therapeutic interventions, events like loss of basophil activation and induction of regulatory lymphocyte subsets and of blocking antibodies have been described. Their functional importance at a clinical level, however, remains to be investigated in detail. Consequently, there is eminent need to understand the process of tolerance development to food allergens and define biomarkers to develop and monitor new treatment strategies for food allergy.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".