Systematic review and network meta-analysis of the risk of malignancy with biologic therapies and selective Janus kinase-1 inhibitors in atopic dermatitis
Bibliographic record
Abstract
Introduction: Atopic dermatitis (AD) is a chronic inflammatory skin disease with multifactorial pathophysiology. Biologic therapies, including dupilumab (IL-4/IL-13 inhibitor) and tralokinumab (IL-13 inhibitor), as well as selective Janus kinase-1 (JAK-1) inhibitors such as upadacitinib and abrocitinib, have been approved for the treatment of moderate to severe AD. However, their association with the incidence of malignancy in AD patients remains uncertain. Aim: We conducted a systematic review and network meta-analysis (NMA) to investigate and compare the indidence and risk of malignancy in individuals with moderate-to-severe AD treated with abrocitinib, upadacitinib, tralokinumab, or dupilumab. Material and methods: Systematic searches were conducted in Ovid MEDLINE and EMBASE that included AD, malignancy, biologic and advanced therapies. The primary outcome was incidence of malignancy in AD patients receiving placebo or at least one of the following advanced therapies: dupilumab, tralokinumab, abrocitinib or upadacitinib. A random-effects NMA was conducted with odds ratios and a frequentist model. Results: Our search identified 11 trials comprising 10097 patients. The NMA did not show any statistically significant association between dupilumab or selective JAK-1 inhibitors and the incidence of malignancy up to an average of 41 weeks of treatment. Conclusions: Our analysis revealed no statistically significant increased risk of malignancy and no significant difference in the incidence of malignancy between selective JAK-1 inhibitors and dupilumab for the treatment of AD up to an average follow-up of 41 weeks. Nevertheless, further prospective studies with longer follow-up periods are warranted to confirm the safety of these therapies and their impact on the risk of malignancy.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".