A Clinical Case of Methotrexate Toxicity
Bibliographic record
Abstract
Methotrexate is a commonly prescribed immunosuppressant and chemotherapy agent, carefully monitored by healthcare providers due to its potential adverse effects. As a result, methotrexate toxicity is relatively rare. We present the case of a 79-year-old man followed in rheumatology for symmetrical polyarthralgia, who inadvertently took methotrexate 10 mg daily, instead of weekly, leading to methotrexate toxicity. The patient presented with erosive mucositis affecting the lateral tongue, buccal mucosa, and hard palate, as well as pustular lesions on the scalp (occipital and cervical regions) extending to the trunk. Laboratory findings revealed pancytopenia and transaminitis, and upper gastrointestinal endoscopy showed erythema and superficial ulcerations in the oropharyngeal region. Methotrexate was discontinued immediately, and the patient was treated with intravenous fluids, filgrastim, and supportive care. This case highlights the importance of early recognition of methotrexate toxicity, as well as the critical role of patient education. It underscores how easily a medication with numerous therapeutic benefits can cause serious adverse outcomes if not taken as prescribed. Effective communication between healthcare providers and patients is essential to ensure medication safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".