Fruit, vegetables and the prevention of cancer: research challenges
Bibliographic record
Abstract
OBJECTIVE: A great deal of epidemiologic evidence has indicated that fruits and vegetables are protective against numerous forms of cancer. However, there are many gaps in our knowledge. \nMETHODS: In this pilot study we reviewed more than 200 cohort and case-control studies to determine the shape of the dose–response relationship (i.e., how the risk reduction per extra serving of fruits and vegetables changes with the actual intake of these foods). We found major barriers to investigating this. \nAs part of this pilot study we also investigated whether specific fruits and vegetables are responsible for the anticancer action of these foods or whether a wide variety is required for optimal protection. If the former is correct, then fruits and vegetables may contain one or a small number of "magic bullets"; if the latter is correct, then a "teamwork" concept may be valid. \nRESULTS: Different findings suggested that the teamwork concept is much more likely. Many studies, especially older ones, have ignored potential confounding variables such as energy intake, alcohol consumption, physical activity, body mass index, smoking, and socioeconomic status (although many \nrecent studies have adjusted for education). Other potential confounders that have generally been ignored are consumption of whole grain cereals and the use of vitamin and mineral supplements. \nCONCLUSIONS: The inverse association between intake of fruits and vegetables and the risk of cancer of the colon, breast, and stomach has generally been much stronger in case-control than in cohort studies. \nWe have no clear explanation for this.
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.
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 | 0.035 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".