Paradoxical psoriasis in pediatric patients: A systematic review
Bibliographic record
Abstract
BACKGROUND: Paradoxical psoriasis occurs in pediatric patients following treatment with biologic agents. These presentations are not well described, and optimal treatment strategies have not been established. OBJECTIVE: To describe the reported rates, demographic characteristics, clinical presentation, and treatment options for TNF-α inhibitor-induced psoriasis. METHODS: Systematic review of published cases and cohort studies of paradoxical psoriasis induced by biologic agents, with specific reference to TNF-α inhibitors. RESULTS: We identified 4564 pediatric patients treated with TNF-α inhibitors, of whom 210 (4.6%) developed paradoxical psoriasis. Infliximab was the drug most likely to induce psoriasis (8.3%), followed by adalimumab (3.3%). Individual-level data were acquired from 129 individuals with a mean age of 13.6 years (SD: 4.0); 45.0% were male. The scalp was the most commonly affected area (47.5%), followed by the ears (30.8%). Most (63.3%) patients were continued on TNF-α inhibitor therapy. Among those who switched TNF-α inhibitors, only 32.0% had complete clearance of their skin lesions. Among patients who were switched to a non-TNF-α inhibitor, 81% had complete clearance of their paradoxical psoriasis. LIMITATIONS: Data were acquired from retrospective studies including case reports and case series. CONCLUSION: TNF-α inhibitor-induced psoriasis is a common adverse effect; however, most patients can continue their original therapy and be managed with skin-directed topical or systemic medications. If a patient requires medication discontinuation, switching to a new TNF-α inhibitor is unlikely to lead to resolution of their skin lesions.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".