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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".