Oral immunotherapy improves the quality of life of adults with food allergy
Bibliographic record
Abstract
BACKGROUND: Oral immunotherapy (OIT) has become the standard of care for children with food allergy (FA) and has substantially improved their quality of life. The effect of OIT on the quality of life in adults, however, has been studied to a much lesser degree. METHODS: Patients with food allergy aged ≥ 18 years who underwent OIT at Shamir Medical Center completed the Food Allergy Quality of Life Questionnaire-Adult Form (FAQLQ-AF) before and at the end of treatment. Adults with FA not undergoing OIT who completed the FAQLQ-AF at 2 time points, served as controls. RESULTS: A total of 44 adults, median age 23.4 years, who underwent OIT for milk (n = 19), egg (n = 2), peanut (n = 9), sesame (n = 6), and tree nuts (n = 8), and 11 controls were studied. The median OIT starting dose was 23.8 mg protein. 33 patients (75%) reached full desensitization within a median of 10.3 months. The FAQLQ-AF baseline scores were comparable between the study and control groups for all items except for Food Allergy related Health (FAH) item in which the study group had a significantly better score (p = 0.02). At the second time point, the study group had significantly better scores in all items (Allergen Avoidance and Dietary Restrictions (AADR), p = 0.02; and Emotional Impact (EI), Risk of Allergen Exposure (RAE), FAH and the Total Score, p < 0.01). The change in scores for the study group was significantly better, statistically and clinically, in AADR, p = 0.04; EI, p < 0.01; RAE, p = 0.01, and in the total score, p = 0.01. CONCLUSIONS: OIT significantly improves quality of life of adults with FA. This finding adds important support for providing OIT in this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".