Guiding Drug Provocation Testing for Ibuprofen Hypersensitivity in a Pediatric Population: Development of the I3A Risk-Stratification Tool
Bibliographic record
Abstract
BACKGROUND: Ibuprofen is a main cause of drug hypersensitivity reactions in children. The gold standard for diagnosis is the drug provocation test (DPT). OBJECTIVE: We aimed to create a clinical risk-stratification tool to guide this high-risk procedure. METHODS: We prospectively recruited children with suspected ibuprofen hypersensitivity between January 2017 and March 2024. Using stepwise bidirectional multivariable logistic regression, we calculated a predictive score for a positive ibuprofen DPT. RESULTS: Eighty-two patients with a median age of 5.9 years (interquartile range: 3.4-11.1 years) had an ibuprofen DPT. Eighteen (22.0%) patients had a positive challenge, with an anaphylactic reaction for 11 (61.1%). The I3A score (acronym for ibuprofen, 3As: angioedema, anaphylaxis, age, cutoff of 3) encompasses the following items: angioedema (2 points), anaphylaxis (1 point), and age at reaction ≥10 years old (1 point). The area under the curve of the I3A score was 0.84, and the optimal cutoff of <3 conferred a sensitivity of 84.4% (95% confidence interval [CI]: 66.7%-100.0%) and a specificity of 83.3% (95% CI: 75.0%-92.2%). The negative predictive value was estimated at 94.7% (95% CI: 90.0%-100.0%), and the positive predictive value at 60.0% (95% CI: 46.2%-76.2%). The relative risk of reacting to a challenge in the group I3A 3-4 compared with 0-2 was 11.4 (95% CI: 3.62%-35.7%, P < .001). Anaphylaxis after DPT was observed in 9 of 25 (36.0% [95% CI: 16.0%-56.0%]) in the high-risk group as compared with 2 of 57 (3.5% [95% CI: 0.0%-8.8%]) in the low-risk group (relative risk 10.3 [95% CI: 2.4%-43.5%]). CONCLUSIONS: We generated a risk-stratification tool to identify children at low risk of reacting to ibuprofen challenges. Further validation is required in external cohorts.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".