Peanut oral immunotherapy in a pediatric allergy clinic: Patient factors associated with clinical outcomes
Bibliographic record
Abstract
BACKGROUND: Additional information is needed to inform optimal patient selection, expected outcomes, and treatment end points for clinical peanut oral immunotherapy (OIT). OBJECTIVE: To provide insight into the optimal patient selection, expected outcomes, and treatment end points for clinical peanut oral immunotherapy by analyzing a real-world peanut OIT cohort. METHODS: Records were reviewed for 174 children undergoing peanut OIT at a pediatric allergy clinic. Patient age, peanut skin prick test results, and peanut-specific immunoglobulin E (sIgE) results, with inclusion of additional foods in OIT, were analyzed for correlations with OIT outcomes. RESULTS: To date, 144 patients have achieved maintenance dosing, 50 of whom transitioned to ad lib twice-weekly peanut ingestion. A total of 30 discontinued OIT. In addition, 47 patients who underwent multifood OIT had no significant difference in reactions (FDR-adjusted P = .48) or time-to-reach maintenance (FDR-adjusted P = .48) compared with those on peanut OIT alone. Age at initiation inversely correlated with achievement of maintenance: 92% of patients 0.5 to less than 5 years, 81% of those 5 to less than 11 years, and 70% of those 11 to less than 18 years reached and continued maintenance (P = .01). Baseline peanut-sIgE level positively correlated with number of reactions during updosing (P < .001) and maintenance (P = .005), though it was not significantly different in patients achieving successful maintenance vs those who discontinued OIT (P = .09). Furthermore, 66% of patients experienced greater than or equal to 1 adverse reaction during OIT. Of those on ad lib peanut ingestion, 2 reported mild reactions after lapses in peanut consumption. CONCLUSION: Clinical peanut OIT has similar outcomes to research protocols. OIT can be successful in older children and those with high peanut-sIgE levels, though these factors affect outcomes. Clinical and laboratory criteria can guide successful transition to intermittent ad lib peanut consumption.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".