Real-world experience of pediatric patients treated with peanut (Arachis hypogaea) allergen powder-dnfp
Bibliographic record
Abstract
BACKGROUND: Peanut (Arachis hypogaea) allergen powder-dnfp (PTAH) is the first oral immunotherapy indicated for children aged 4 to 17 years with peanut allergy. There are limited real-world data on patients treated with PTAH. OBJECTIVE: To characterize pediatric patients treated with PTAH and associated treatment patterns in US clinical practice. METHODS: US-based physicians with allergy and immunology training treating patients with peanut allergy aged 4 to 17 years with PTAH were recruited from an existing physician panel and completed an online case report form (October to December 2021) with data abstracted from patient medical charts. Physician practice circumstances, patient characteristics, and PTAH treatment patterns were reported. Time to reach the 300-mg dose and treatment persistence were assessed using Kaplan-Meier analysis. RESULTS: A geographically balanced sample of 43 physicians contributed data for 118 demographically diverse pediatric patients. Patients had heterogeneous diagnostic test results, with a wide range of peanut-specific immunoglobulin E levels; 6.8% received an oral food challenge. During the updosing phase, there were no temporary interruptions and 5.1% of the patients required downdosing. Patients reached the 300-mg dose at a median of 21.3 weeks post-initiation. The rate of PTAH persistence at 24 weeks was 93.4%. Only 1 patient discontinued treatment because of treatment-related systemic allergic symptoms, and the remaining discontinuations were for reasons other than treatment-related symptoms. Prophylactic antihistamines were used by 33.9% of the patients to prevent PTAH adverse effects. CONCLUSION: PTAH was prescribed in demographically diverse patients with a wide range of peanut-specific immunoglobulin E levels. Treatment persistence with PTAH was high in this study population, with a small number of patients experiencing treatment modification.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".