Recent advances in clinical practice: colorectal cancer chemoprevention in the average-risk population
Bibliographic record
Abstract
Colorectal cancer (CRC) is one of the most common and lethal malignancies in Western countries. Its development is a multistep process that spans more than 15 years, thereby providing an opportunity for prevention and early detection. The high incidence and mortality rates emphasise the need for prevention and screening. Many countries have therefore introduced CRC screening programmes. It is expected, and preliminary evidence in some countries suggests, that this screening effort will decrease CRC-related mortality rates. CRC prevention involves a healthy lifestyle and chemoprevention-more specifically, oral chemoprevention that can interfere with progression from a normal colonic mucosa to adenocarcinoma. This preventive effect is important for individuals with a genetic predisposition, but also in the general population. The ideal chemopreventive agent, or combination of agents, remains unknown, especially when considering safety during long-term use. This review evaluates the evidence across 80 meta-analyses of interventional and observational studies of CRC prevention using medications, vitamins, supplements and dietary factors. This review suggests that the following factors are associated with a decreased incidence of CRC: aspirin, non-steroidal anti-inflammatory drugs, magnesium, folate, a high consumption of fruits and vegetables, fibre and dairy products. An increased incidence of CRC was observed with frequent alcohol or meat consumption. No evidence of a protective effect for tea, coffee, garlic, fish and soy products was found. The level of evidence is moderate for aspirin, β-carotene and selenium, but is low or very low for all other exposures or interventions.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".