Methotrexate-Induced Cutaneous Ulcers in a Nonpsoriatic Patient: Case Report and Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Methotrexate is a mainstay of treatment for autoimmune conditions such as rheumatoid arthritis and psoriasis. Methotrexate has numerous potential side effects and, in rare circumstances, can lead to cutaneous ulceration. Methotrexate can cause skin ulceration, and stopping this medication can lead to complete healing of the ulcerated lesion. OBSERVATIONS: A 67-year-old man with rheumatoid arthritis on long-term methotrexate therapy presented to hospital with ulcers on his hands, elbows, and lower extremities. He had no history of psoriasis. Shortly after admission, the patient was noted to have pancytopenia. A bone marrow biopsy showed a hypocellular marrow. Both the cutaneous ulcers and the hypocellular marrow were thought to be induced by methotrexate. The ulcerated areas were biopsied, and histopathology showed no evidence of vasculitis. After 1 month of rehabilitative skin care, the patient's ulcers healed almost completely and his bone marrow suppression recovered. CONCLUSION: We report the fifth case of methotrexate-induced cutaneous ulceration in a nonpsoriatic patient and review the literature on this unusual drug reaction. Methotrexate can induce cutaneous ulceration in nonpsoriatic patients and should be considered a potential cause of ulceration in patients treated with this antimitotic agent.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".