Pyoderma gangrenosum and systemic treatment
Bibliographic record
Abstract
Pyoderma gangrenosum (PG) is a rare disease where a person's own immune system causes painful skin ulcers, due to a complex and poorly understood mechanism. PG is extremely rare, affecting 3–10 people per million people per year globally; however, when it does occur it bears significant impact on quality of life and increases the risk of death. Current treatment includes a combination of systemic steroids (systemic meaning taken inside the body rather than applied to the skin), immune suppressing drugs, and topical (applied to the skin) treatments, but after decades of research we still do not know the best (or “gold‐standard”) treatment regimen. In this study, authors from Canada aimed to systematically review all of the available research evidence on systemic treatments for PG in order to determine the best treatment regimen. After critically reviewing 3326 research studies, 41 studies were deemed relevant to this research question. What they found was that systemic corticosteroids, cyclosporine, and biologic agents were most commonly studied and were most effective at treating PG. Often, these were combined with topically applied drugs such as steroid cream. Nonetheless, amongst included studies, there was a low cure rate of 15–50% after months of treatment; while cure was sometimes achieved after two to four months, there was a high rate of recurrence of ulcers. Furthermore, the authors found that most studies on PG therapies are of poor quality and thus call for higher quality research in this field.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".