Effect of statin therapy on colorectal cancer
Bibliographic record
Abstract
Hydroxymethylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, also called statins, are commonly prescribed medications that lower serum cholesterol and decrease cardiac morbidity and mortality. They also possess beneficial effects beyond their cholesterol-lowering properties. Preclinical data suggest statins exhibit pleiotropic antineoplastic effects in a variety of tumours, but clinical studies have provided conflicting data as to whether statins influence the risk of cancer. The biological underpinning of potential effects of statins in colorectal cancer and their role in its prevention or as adjuvant therapy are reviewed. Following a meta-analysis of both randomised clinical trials and epidemiological studies, it is concluded that available clinical data only support a modest, although statistically significant, protective effect of statins in colorectal cancer. Statins may impact on outcomes by decreasing the invasiveness or metastatic properties of colorectal cancer. The data supporting these hypotheses, however, are few and further studies are required to better assess these hypotheses. Statins may also exert a beneficial effect on colorectal cancer by sensitising the tumour to chemotherapeutic agents. Further research is needed to better define the role of statins in overcoming chemoresistance. The combination of statins with other drugs, such as low-dose aspirin or safer non-steroidal anti-inflammatory medications, may be useful in both the prevention and treatment of colorectal cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".