Efficacy of Tumor Necrosis Factor Inhibitors for Refractory Leg Ulcers in Cutaneous Polyarteritis Nodosa: A Case Series
Bibliographic record
Abstract
OBJECTIVE: Cutaneous polyarteritis nodosa (cPAN) is a rare necrotizing vasculitis that affects primarily small- to medium-sized arteries in subcutaneous tissue. cPAN is often characterized by a chronic and relapsing disease that presents with skin ulcers, livedo, and painful erythema. In this study, we evaluated the efficacy of tumor necrosis factor inhibitor (TNFi) treatment for cPAN-associated refractory leg ulcers. METHODS: This retrospective study was conducted between 2016 and 2023 at 3 medical institutions in Japan and targeted patients with cPAN presenting with refractory leg ulcers who were treated with TNFi. The diagnosis of cPAN was histologically confirmed, and patients with secondary PAN were excluded. Data on the clinical background, treatment, ulcer status, and glucocorticoid (GC) dosage were collected, and the therapeutic efficacy of the treatment was evaluated. RESULTS: Ten patients were included, with a mean age of 51 years, and 9 were female. All patients presented with recurrent leg ulcers. TNFi included adalimumab (5 cases), etanercept (4 cases), and infliximab (1 case). Complete epithelialization of the leg ulcers was achieved in all patients, and the average GC dose was successfully reduced from 20 mg/day to 3.5 mg/day. Additionally, 5 patients achieved a GC-free status. No serious adverse events were observed in any of the patients. CONCLUSION: TNFi showed therapeutic efficacy for cPAN-associated refractory leg ulcers and enabled ulcer epithelialization and significant GC dose reduction. These findings support the utility of TNFi in the management of refractory leg ulcers in cPAN, highlighting the need for further large-scale studies to validate the results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".