Recent advances in clinical practice: colorectal cancer chemoprevention in the average-risk population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it