Infection Risk in Dermatology Patients Receiving Next-Generation Medication: A Meta-Analysis of JAK Inhibitors and Biologics
Bibliographic record
Abstract
Background/Objectives: Next-generation drugs, such as JAK inhibitors and biologics, have proved to be very effective treatment choices in several autoimmune and autoinflammatory skin disorders. However, these drugs are not without risk. Due to their immune-modulating properties, these drugs may pose a risk of infection, which could vary between drug target, disorder type and pathogen. Our goal was to determine infection risk and how it may vary by drug target, pathogen and skin disorder, namely psoriasis, atopic dermatitis, alopecia areata, vitiligo and hidradenitis suppurativa. Methods: We performed a systematic search and meta-analysis where we extracted the rates of different infections from the adverse events of each trial that were found and met our inclusion criteria. Results: We found significant associations in psoriasis and atopic dermatitis where infection risk varied by drug, skin condition and pathogen type. We specifically found that there was an increased risk of viral infection for patients with atopic dermatitis with both JAK inhibitors and biologics. We also found an increased risk of fungal infections in psoriasis patients receiving targeted therapies. Lastly, we observed a decreased risk of bacterial infections in atopic dermatitis with dupilumab specifically. Additionally, there was a significantly higher incidence of herpes simplex infections in atopic dermatitis patients with target-selective JAK inhibitors, while no increased risk was observed with herpes zoster. Conclusions: There is a varied risk with these next-generation medications that needs to be considered when determining treatment regime.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".