The role of anti-tumour necrosis factor in wound healing: A case report of refractory ulcerated necrobiosis lipoidica treated with adalimumab and review of the literature
Bibliographic record
Abstract
Necrobiosis lipoidica is a chronic granulomatous disease historically associated with diabetes. Necrobiosis lipoidica commonly presents with erythematous papules or plaques on the anterior lower extremities, which can be ulcerated in up to 30% of patients. The pathophysiology of necrobiosis lipoidica is unknown but proposed to be predominantly linked to microangiopathy. No treatment option for necrobiosis lipoidica has shown consistent efficacy. Previous case reports have shown immune-modulating agents to be reasonable treatment options for ulcerative necrobiosis lipoidica. However, evidence for the tumour necrosis factor-alpha inhibitor, adalimumab, is limited and contradictory. We report a case of a 74-year-old type 2 diabetic female with a 2-year history of multiple ulcerated necrobiosis lipoidica plaques resistant to topical and systemic therapy. Treatment with adalimumab showed complete re-epithelization of all ulcers by week 28. Adalimumab may be an effective treatment option for ulcerated necrobiosis lipoidica that has failed traditional therapies. Further reports of adalimumab treatment of necrobiosis lipoidica and other chronic inflammatory wounds are needed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".