Addressing common questions on food oral immunotherapy: a practical guide for paediatricians
Bibliographic record
Abstract
Food allergy has been increasing in prevalence in most westernised countries and poses a significant burden to patients and families; dietary and social limitations as well as psychosocial and economic burden affect daily activities, resulting in decreased quality of life. Food oral immunotherapy (food-OIT) has emerged as an active form of treatment, with multiple benefits such as increasing the threshold of reactivity to the allergenic food, decreasing reaction severity on accidental exposures, expanding dietary choices, reducing anxiety and generally improving quality of life. Risks associated with food immunotherapy mostly consist of allergic reactions during therapy. While the therapy is generally considered both safe and effective, patients and families must be informed of the aforementioned risks, understand them, and be willing to accept and hedge these risks as being worthwhile and outweighed by the anticipated benefits through a process of shared decision-making. Food-OIT is a good example of a preference-sensitive care paradigm, given candidates for this therapy must consider multiple trade-offs for what is considered an optional therapy for food allergy compared with avoidance. Additionally, clinicians who discuss OIT should remain increasingly aware of the growing impact of social media on medical decision-making and be prepared to counter misconceptions by providing clear evidence-based information during in-person encounters, on their website, and through printed information that families can take home and review.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.068 | 0.039 |
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".