Applying the Precautionary Principle to Nutrition and Cancer
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Research has identified certain foods and dietary patterns that are associated with reduced cancer risk and improved survival after cancer diagnosis. This research has formed the basis for dietary guidance issued by cancer organizations. Unfortunately, gaps within nutrition research have made it difficult to make recommendations in some areas. This review specifies suggested dietary guidance in which evidence of a dietary influence on cancer risk is substantial, even if not conclusive. Evidence summaries within the review are based on the 2007 report of the World Cancer Research Fund/American Institute for Cancer Research. This review also describes advantages and disadvantages of following the suggested dietary guidance and includes putative mechanisms involved in cancer progression. MAIN OUTCOMES AND RESULTS: Suggested dietary guidance where evidence is sufficiently compelling include (1) limiting or avoiding dairy products to reduce the risk of prostate cancer; (2) limiting or avoiding alcohol to reduce the risk of cancers of the mouth, pharynx, larynx, esophagus, colon, rectum, and breast; (3) avoiding red and processed meat to reduce the risk of cancers of the colon and rectum; (4) avoiding grilled, fried, and broiled meats to reduce the risk of cancers of the colon, rectum, breast, prostate, kidney, and pancreas; (5) consumption of soy products during adolescence to reduce the risk of breast cancer in adulthood and to reduce the risk of recurrence and mortality for women previously treated for breast cancer; and (6) emphasizing fruits and vegetables to reduce risk of several common forms of cancer. CONCLUSION: By adopting the precautionary principle for nutrition research, this review aims to serve as a useful tool for practitioners and patients.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.034 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| 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".