Do thiopurines prevent advanced colorectal neoplasia in patients with inflammatory bowel disease or is this an unanswerable question?
Bibliographic record
Abstract
van Schaik FDM, van Oijen MGH, Smeets HM, et al. Thiopurines prevent advanced colorectal neoplasia in patients with inflammatory bowel disease. Gut 2011 [Epub ahead of print]. A question of much interest over the past number of years pertains to whether mesalamine has a chemopreventive effect in preventing the development of dysplasia or colorectal cancer and a clear answer to this query continues to elude us, as data have been conflicting.1,2 Although a number of potential mechanisms underlying the proposed chemopreventive effect have been proposed, one key mechanism may be an ability to abrogate colonic inflammation, which may be a highly relevant risk for the development of dysplasia or colorectal cancer.3,4 Thus, it stands to reason that any efficacious therapy for inflammatory bowel disease (IBD), such as thiopurines, could potentially have a similar benefit. To evaluate this question, van Schaik et al analyzed data from a nationwide pathology database that was linked to a Dutch health insurance database to evaluate whether the use of thiopurines was associated with a reduced risk of advanced colonic neoplasia (AN), defined as high-grade dysplasia (HGD) or colorectal cancer (CRC).
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.029 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".