Long‐term outcome of using allopurinol co‐therapy as a strategy for overcoming thiopurine hepatotoxicity in treating inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Hepatotoxicity results in the withdrawal of thiopurines drugs, azathioprine (AZA) and mercaptopurine (MP), in up to 10% of patients with inflammatory bowel disease. Our group previously demonstrated that allopurinol with AZA/ciclosporin/steroid 'triple therapy' improved renal graft survival. AIM: To confirm the hypothesis that allopurinol may alleviate thiopurine hepatotoxicity by similar mechanisms as proposed in our renal study. METHODS: Unselected patients with acute thiopurine hepatotoxicity were offered allopurinol co-therapy with low-dose AZA or MP. The starting AZA/MP dose was determined by thiopurine methyltransferase (TPMT) activity (two patients were intermediate TPMT); then this dose was reduced to 25% for allopurinol co-therapy. Response to treatment was assessed by clinical severity indices, endoscopy and blood tests. RESULTS: Of 11 patients (three Crohn's disease, eight ulcerative colitis) treated, nine (82%) remain in long-term remission (median 42 months) with normal liver tests. One patient also successfully bypassed flu-like symptoms. Two stopped: one nausea, one abnormal liver function (stealosis on biopsy). Leucopenia occurred in two cases and resolved with minor dose reductions. CONCLUSIONS: Allopurinol co-therapy with low-dose AZA/MP can alleviate thiopurine hepatotoxicity. It appears safe and effective for long-term use, but requires monitoring for myelotoxicity. Assessing the TPMT activity helps tailor the AZA/MP doses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".