Development and acceptability of a decision‐aid for food allergy oral immunotherapy in children
Bibliographic record
Abstract
BACKGROUND: Limited decision-support tools are available to help shared decision-making (SDM) regarding food oral immunotherapy (OIT) initiation. No current tool covers all foods, forms, and pediatric ages for which OIT is offered. METHODS: In compliance with International Patient Decision Aid Standards criteria, this pediatric decision-aid comparing OIT versus avoidance was developed in three stages. Nested qualitative data assessing OIT decisional needs were supplemented with evidence-synthesis from the OIT literature to create the prototype decision-aid content. This underwent iterative development with food allergy experts and patient advocacy stakeholders until unanimous consensus was reached regarding content, bias, readability, and utility in making a choice. Lastly, the tool underwent validated assessment of decisional acceptability, decisional conflict, and decisional self-efficacy. RESULTS: The decision-aid underwent 5 iterations, resulting in a 4-page written aid (Flesch-Kincaid reading level 6.1) explaining therapy choices, risks and benefits, providing self-rating for attribute importance for the options and self-assessment regarding how adequate the information was in decision-making. A total of n = 135 caregivers of food-allergic children assessed the decision-aid, noting good acceptability, high decisional self-efficacy (mean score 85.9/100) and low decisional conflict (mean score 20.9/100). Information content was rated adequate and sufficient, the therapy choices wording balanced, and presented without bias for a "best choice." Lower decisional conflict was associated with caregiver-reported anaphylaxis. CONCLUSIONS: This first pediatric OIT decision-aid, agnostic to product, allergen, and age has good acceptability, limited bias, and is associated with low decisional conflict and high decisional self-efficacy. It supports SDM in navigating the decision to start OIT or continue allergen avoidance.
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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 | 0.032 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".