Review of Network Meta-Analyses on the Efficacy of Chemopreventive Agents on Colorectal Adenomas and Cancer
Bibliographic record
Abstract
BackgroundColorectal cancer (CRC) is the third most diagnosed cancer and the second leading cause of cancer-related death worldwide. Colorectal adenomas (CRAs) are a crucial precursor for CRC and a target for preventive strategies. Recent network meta-analyses (NMAs) of randomized controlled trials (RCTs) suggest that chemopreventive agents (CPAs) are associated with reductions in CRC incidence. However, the quality of this evidence is low due to significant variability in the methods and types of studies assessed.PurposeOur study reviewed the efficacy and safety of CPAs on CRAs or CRCs evaluated in NMAs of RCTs and assessed the quality of all published NMAs on CPAs.Research DesignWe searched PubMed, Embase, and Cochrane Library for studies published from inception to July 29, 2024. We included all NMAs assessing the efficacy and safety of CPAs on CRC in both average-risk (general population) and high-risk (previous history of adenoma/CRC) populations. ResultsNine NMAs comparing 15 different interventions were included. Aspirin and non-aspirin non-steroidal anti-inflammatory drugs (NSAIDs) such as celecoxib were the most studied. Aspirin demonstrated efficacy against the development of any CRA and low-dose aspirin was consistently more protective than high-dose aspirin. However, the effect of aspirin against advanced CRA was not statistically significant. Concerns for long-term aspirin use included an increased risk of gastrointestinal bleeding and ulceration, but when evaluating all serious adverse events (SAEs), aspirin users did not have an increased risk compared to controls. Non-aspirin NSAIDs showed better efficacy against advanced CRA. However, the use of non-aspirin NSAIDs such as celecoxib was associated with significantly increased risk of SAEs, particularly cardiovascular disease events.ConclusionsConsidering the balance of efficacy and safety, low-dose aspirin is currently the best option for chemoprevention of CRA/CRC. Future research is needed to better characterize the patient subgroups that benefit most and to develop new, more effective CPAs.
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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 | 0.100 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".