Preventive effects of 13 different drugs on colorectal cancer: a network meta-analysis
Bibliographic record
Abstract
Introduction: The aim of the study was to evaluate the preventive effect of 13 drugs on colorectal cancer (CRC) and guide the clinical application of these drugs. Material and methods: PubMed, Web of Science, Embase, Cochrane Library, and China National Knowledge Infrastructure were searched for randomized controlled trials (RCTs) and cohort studies. The Cochrane bias risk assessment tool and the Newcastle-Ottawa Scale quality evaluation tool were used to evaluate the quality of the included RCTs and cohort studies. The funnel plot was used to analyze publication bias. A network meta-analysis of the extracted data was conducted using Stata16.0 software. Results: A total of 57 studies (34 RCTs and 23 cohort studies) involving 82719 participants were included. The network meta-analysis revealed that the quality of the included studies was good; the funnel plot showed no obvious publication bias. The network meta-analysis showed that the preventive effect of 13 drugs on CRC was better than that of the placebo. Allopurinol (SUCRA: 97.2%) was found to have the best effect, followed by berberine (SUCRA: 89.9%), non-aspirin NSAIDs (SUCRA: 84.5%), statins (SUCRA: 66.5%), metformin (SUCRA: 66.3%), calcium (SUCRA: 48.9%), mesalazine (SUCRA: 44.5%), ursodeoxycholic acid (SUCRA: 42.6%), vitamin D (SUCRA: 41.4%), mercaptopurine (SUCRA: 39.4%), aspirin (SUCRA: 30.4%), folic acid (SUCRA: 24.9%), and eicosapentaenoic acid (SUCRA: 16.3%). Conclusions: The preventive effect of allopurinol on CRC was better than that of the other 13 drugs. These results can help doctors and patients understand the preventative effects of these drugs more intuitively and provide an evidence-based basis for the clinical application of these drugs.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.058 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".