Thiopurines in the Management of Crohn’s Disease: Safety and Efficacy Profile in Patients with Normal TPMT Activity—A Retrospective Study
Bibliographic record
Abstract
Background and Aims. Thiopurines are used in the treatment of Crohn's disease (CD) and thiopurine S-methyltransferase (TPMT) activity can guide thiopurine dosing to avoid adverse events. This retrospective study evaluated the safety and efficacy of starting thiopurines at low dose versus full dose in patients with CD and normal TPMT. Methods. This was a single center retrospective study including adult CD patients with normal TPMT levels (≥25 nmol/hr/g Hgb) who were followed for 1 year. Patients started at full dose of azathioprine (2-2.5 mg/kg) or 6-mercaptopurine (1-1.5 mg/kg) were compared to patients started at low dose. Harvey-Bradshaw index, treatment failure, and drug-related adverse events were recorded. Results. Our study included 134 patients. Both groups had similar incidences of drug-related adverse events and discontinuation of therapy due to side effects. Fifty-six percent of all adverse events occurred within 31 days and 92% occurred within 3 months of therapy. Clinical response favored the full-dose group at 6 months (69% versus 27%, p = 0.0542). Conclusions. Our study indicates that it is safe to start patients on full-dose thiopurine when they have a normal TPMT given its very similar toxicity profile to patients started on low dose. This may also positively impact efficacy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".