Nonsteroidal Anti-inflammatory Drugs for Chemoprevention in Patients With Familial Adenomatous Polyposis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and Aims Published literature shows mixed reports of the benefits of nonsteroidal anti-inflammatory drugs (NSAIDs) on reducing colorectal polyps in patients with familial adenomatous polyposis (FAP). We conducted a systematic review and performed a meta-analysis to assess the impact of NSAIDs on colorectal polyp burden in patients with FAP. Methods We searched PubMed, EMBASE, and Cochrane for randomized controlled trials (RCTs) comparing the effect of NSAIDs vs placebo on the percent change in polyp number and polyp size in patients with FAP. Mean differences between the 2 study arms were pooled using RevMan. The risk of bias (RoB) was assessed using the Cochrane Risk of Bias tool for RCTs, and certainty in the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation methodology. Results The search strategy identified 1021 studies, out of which we included 8 RCTs with a total of 279 patients. Treatment for 6.4 ± 2.2 months with NSAIDs reduced polyp numbers by −17.4% (95% confidence interval −26.41%, −8.29%) (low certainty [I 2 89%] due to imprecision and issues with RoB) and polyp size by −15.9% (95% confidence interval −24.98%, −6.73%) (very low certainty (I 2 84%) due to imprecision, inconsistency, and issues with RoB). The most common gastrointestinal adverse events reported were stomatitis, diarrhea, and abdominal pain. Side effects leading to drug discontinuation were gastroenteritis and drug allergy. Conclusion Short-term use of NSAIDs reduced polyp number and polyp size but with low to very low certainty of evidence. Further large multicenter studies are needed to further explore NSAIDs as a chemopreventive measure in patients with FAP.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".