Treatment of Ulcerative Necrobiosis Lipoidica with Topical Calcineurin Inhibitor: Case Report and Literature Review
Bibliographic record
Abstract
Background: Ulcerative necrobiosis lipoidica (UNL) is an uncommon disease, which is frequently recalcitrant to available therapies. It is characterized by well-defined, ulcerated plaques with indurated borders and atrophic centers. Multiple therapeutic options have been described, with variable success rates. Objective: To report the efficacy of using topical tacrolimus in treating UNL. Method: Topical tacrolimus was used in the treatment of two patients with UNL. Result: Topical tacrolimus is effective in treating UNL. Conclusion: Topical tacrolimus is a reasonably effective choice in treating UNL. Contexte: La nécrobiose lipoïdique ulcérée (NLU) est une maladie rare, qui est souvent réfractaire au traitement. Elle se caractérise par des placards ulcérés et bien définis, des bords indurés et des centres atrophiques. Différentes formes de traitement ont été utilisées et se sont soldées par des taux variables de réussite. Objectif: L'étude visait à faire état de l'efficacité du tacrolimus topique dans le traitement de la NLU. Méthode: Nous avons fait usage de tacrolimus topique dans le traitement de la NLU chez deux patients. Résultat: Le tacrolimus topique s'est montré efficace dans le traitement de la NLU. Conclusion: Le tacrolimus topique est un moyen relativement efficace de traitement de la NLU.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".