Low Infection Rates With Long‐Term Dupilumab Treatment in Patients Aged 6 Months to 5 Years: An Open‐Label Extension Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate long-term infection rates in children aged 6 months to 5 years with moderate-to-severe atopic dermatitis (AD) treated with dupilumab. METHODS: This was a post hoc analysis of an ongoing open-label extension (OLE) study of dupilumab. Pediatric patients aged 6 months to 5 years with moderate-to-severe AD who had previously taken part in the LIBERTY AD PRESCHOOL phase 2 and 3 clinical trials received weight-based subcutaneous dupilumab every 2 or 4 weeks. Exposure-adjusted infection rates after a median dupilumab exposure of 52 weeks are compared with data from the earlier randomized, placebo-controlled, 16-week LIBERTY AD PRESCHOOL phase 3 trial. RESULTS: Infection rates were overall lower in the OLE study compared with the dupilumab and placebo groups in the earlier 16-week trial, including total infections (101.0 patients/100 patient-years [PY]), nonherpetic skin infections (22.7 patients/100PY), herpetic infections (7.3 patients/100PY), and nonskin infections (92.9 patients/100PY). The frequency of severe and serious infections was low (3.1 patients/100PY), compared with 17.1 placebo-treated patients/100PY and 0 dupilumab-treated patients in the earlier 16-week trial, and no infections leading to treatment discontinuation were observed. Systemic anti-infective medication use (58.9 patients/100PY) was lower in the OLE study compared with both the dupilumab and placebo groups in the 16-week trial. CONCLUSION: Overall, reduced infection rates are observed in infants and young children with moderate-to-severe AD treated with dupilumab long-term, supporting the known safety profile of dupilumab.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".