Clinical Features and Therapeutic Outcomes in Pyoderma Gangrenosum: A Prospective Cohort Study
Bibliographic record
Abstract
ABSTRACT Background Pyoderma gangrenosum (PG) is a rare neutrophilic dermatosis highly associated with systemic comorbidities. Accurate diagnosis and treatment remain challenging due to its rarity and clinical mimickers. Objectives To evaluate demographic, clinical features and treatment outcomes in patients referred with suspected PG at a tertiary wound care centre. Methods A prospective observational study was conducted of patients referred for suspected PG between April 2021 and June 2023. Demographic, clinical and laboratory data, including biopsies and tissue cultures, were collected. Patients were categorised as having PG vs alternative diagnoses based on diagnostic criteria from Su, Maverakis and the PARACELSUS score. Results Of 60 patients (female, 51.7%; mean [SD] age 52.9 [14.0] years), 44 (73.3%) met at least one published diagnostic criterion for PG. The most common comorbidity was IBD (63.6%), followed by inflammatory arthritis (25.0%) and monoclonal gammopathy of undetermined significance (MGUS) (4.5%). Systemic corticosteroids were used by 40.9% of patients before referral, but steroid‐sparing agents and biologics were the primary treatments after consultation. Biologics and steroid‐sparing agents were associated with significant improvement in disease activity using the PGA score (average score decreased from 2.3 to 1.5, p < 0.005), with 40.9% achieving complete remission. Elevated serum inflammatory markers and faecal calprotectin were observed in PG patients but absent in non‐PG cases. Conclusions These prospective data support previously published rates of association between PG and systemic comorbidities. Steroid‐sparing anti‐neutrophilic and targeted therapies are effective steroid‐sparing strategies in managing PG. Small sample size and referral bias may limit generalisability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".