Morphea, Eosinophilic Fasciitis and Cancer: A Scoping Review
Bibliographic record
Abstract
Morphea is an autoimmune fibrotic skin disease. Eosinophilic fasciitis (EF) is considered to belong to the severe spectrum of morphea. We conducted a scoping review assessing the risk of secondary cancer among morphea/EF patients, paraneoplastic morphea/EF and morphea/EF developing secondary to cancer therapy. The search was conducted using MEDLINE, Embase, Cochrane databases for articles published from inception to September 2022 following the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines with no language or date restrictions. Two hundred and one studies were included. Of these, 32 studies reported on secondary cancer in morphea/EF patients, 45 on paraneoplastic morphea/EF and 125 on cancer-treatment-induced morphea/EF. While the current evidence remains limited, data suggest an increased risk of secondary cutaneous and possibly pancreatic malignancy in morphea patients, particularly the generalized subtype. There were insufficient data for EF. On the other hand, paraneoplastic morphea was anecdotal, whereas several observational studies suggested that ~10% of EF cases may be paraneoplastic, primarily in the context of hematologic malignancies. Radiotherapy-induced morphea is rare, seen in ~0.2% of treated patients and is usually localized to the treatment site, except in patients with pre-existing autoimmunity. While chemotherapy-induced cases are reported, immunotherapy morphea/EF cases are emerging and are preferentially seen with PD-1 and not CTLA-4 inhibitors. This study is limited by the type of articles included (case reports, case series and observational studies), and hence, additional research on this important topic is needed.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".