Recent advances in managing and understanding pyoderma gangrenosum
Bibliographic record
Abstract
Pyoderma Gangrenosum (PG) is a rare neutrophilic dermatosis with multiple different clinical presentations and associated comorbidities. PG has historically been a challenging disorder to diagnose, leading to the development of new diagnostic criteria rather than the traditional approach of a diagnosis of exclusion. The pathophysiology is thought to involve both innate and adaptive immune system dysregulation, neutrophilic abnormalities, environmental, and genetic factors. As of today, no gold standard therapy exists for the treatment of PG, and the literature is restricted to mainly case reports, case series, and 2 small randomized clinical trials. Topical, systemic, and biologic therapy, as well as adequate analgesia and proper wound care all play a role in the management of PG. Recent studies have identified additional cytokines and signalling cascades thought to be involved in the pathogenesis of PG, ultimately leading to the development of new targeted therapies. This review will focus on recent advances in the pathophysiology, clinical presentation and associated comorbidities, diagnosis, and management of PG.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".