Reactive Granulomatous Dermatitis: A Descriptive Study of 10 Patients
Bibliographic record
Abstract
BACKGROUND: Reactive granulomatous dermatitis (RGD) is a rare and misunderstood skin disorder. It includes interstitial granulomatous dermatitis and palisaded neutrophilic and granulomatous dermatitis: 2 entities of the same spectrum. Multiple associations are described with RGD in the literature, including autoimmune diseases, malignancy, and drugs. OBJECTIVE: To report and describe the suspected associations with RGD at the time of diagnosis and in the following year. METHODS: between January 2000 and December 2020. Collected data include the systemic diseases (autoimmune disease, malignancy) and suspected drugs, in addition to the clinical presentation and prescribed treatments. RESULTS: Out of the 10 patients with RGD, 7 patients were known to have an autoimmune disease at the time of diagnosis. They either had inflammatory arthritis (3/10) or inflammatory bowel disease (4/10). There was a clinical suspicion of a possible association with a tumor necrosis factor (TNF) inhibitor in 2 of these 7 patients. Among the 3 patients with idiopathic RGD at the time of diagnosis, 1 patient developed a high-grade B-cell lymphoma 6 months later. There was no new association identified in the following year for patients with a known autoimmune condition. CONCLUSION: This descriptive study supports RGD and its previously described systemic associations, particularly autoimmune diseases, malignancy, and certain drugs (specifically TNF inhibitors). The majority of patients already had one of these associations identified at the time of histopathological diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".