Letter: thiopurine blood monitoring for patients with inflammatory bowel disease
Bibliographic record
Abstract
I read with great interest, the article by Chouchana and colleagues.1 The authors summarised the current evidence examining the efficacy and dose and nondose-related toxicity of thiopurine in patients with inflammatory bowel disease (IBD). They suggested an evidence-based algorithm to help with guiding practitioners in using thiopurine for patients with IBD. They recommended monitoring of blood count regularly during therapy as myelotoxicity can happen at any time during therapy beginning from 2 weeks after introduction of thiopurine.2 In the algorithm provided, the authors recommended regular blood testing for blood cell counts and liver tests. Unfortunately, the authors did not provide any recommendations regarding how frequently the blood testing should be performed. Regular blood testing can be an obstacle in initiating and maintain thiopurine especially in children with IBD. Currently, there is no consensus regarding the frequency of blood testing for thiopurine monitoring. Several practitioners would do blood testing every week for the first 4 weeks after initiating thiopurine therapy, followed by fortnight blood testing for 1 month, then monthly blood testing for a variable duration, and then 3-month blood testing, until thiopurine is stopped. Although there is no good evidence to suggest clear benefits of thiopurine therapy after 18 months of maintaining IBD remission, it is very common for patients with IBD to be on thiopurine for 5 years.3 Continuoing blood monitoring for such a long duration can create major anxiety especially in children with IBD. Large well-designed studies are needed to address this important issue. Declaration of personal and funding interests: None.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.022 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".