Preparing Patients for Oral Immunotherapy (PPOINT): International Delphi consensus for procedural preparation and consent
Bibliographic record
Abstract
BACKGROUND: Despite the promise of oral immunotherapy (OIT) to treat food allergies, this procedure is associated with potential risk. There is no current agreement about what elements should be included in the preparatory or consent process. OBJECTIVE: We developed consensus recommendations about the OIT process considerations and patient-specific factors that should be addressed before initiating OIT and developed a consensus OIT consent process and information form. METHODS: We convened a 36-member Preparing Patients for Oral Immunotherapy (PPOINT) panel of allergy experts to develop a consensus OIT patient preparation, informed consent process, and framework form. Consensus for themes and statements was reached using Delphi methodology, and the consent information form was developed. RESULTS: The expert panel reached consensus for 4 themes and 103 statements specific to OIT preparatory procedures, of which 76 statements reached consensus for inclusion specific to the following themes: general considerations for counseling patients about OIT; patient- and family-specific factors that should be addressed before initiating OIT and during OIT; indications for initiating OIT; and potential contraindications and precautions for OIT. The panel reached consensus on 9 OIT consent form themes: benefits, risks, outcomes, alternatives, risk mitigation, difficulties/challenges, discontinuation, office policies, and long-term management. From these themes, 219 statements were proposed, of which 189 reached consensus, and 71 were included on the consent information form. CONCLUSION: We developed consensus recommendations to prepare and counsel patients for safe and effective OIT in clinical practice with evidence-based risk mitigation. Adoption of these recommendations may help standardize clinical care and improve patient outcomes and quality of life.
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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 | 0.269 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".