Co-consumption of Vegetables and Fruit, Whole Grains, and Fiber Reduces the Cancer Risk of Red and Processed Meat in a Large Prospective Cohort of Adults from Alberta’s Tomorrow Project
Bibliographic record
Abstract
We examined whether co-consumption of red and processed meat with key foods items and food constituents recommended for cancer prevention (vegetables and fruit, whole grains, and fiber) mitigates cancer incidence. In a prospective cohort of 26,218 adults aged 35-69 years at baseline, dietary intake was collected through 124-item past-year food frequency questionnaire. Incidence of all-cause and 15 cancers previously linked to red and processed meat intake was obtained through data linkage with a cancer registry (average follow-up 13.5 years). Competing risk Cox Proportional Hazard models estimated cancer risk and Accelerated Failure Time models estimated time-to-cancer occurrence for different combinations of intake levels while considering mortality from vital statistics and established confounders. Co-consumption of low vegetables and fruit intake with high processed meat was associated with higher incidence of all-cause and 15 cancers (men: HR = 1.85, 1.91; women: HR = 1.44, 1.49) and accelerated time-to-cancer occurrence (men: 6.5 and 7.1 years and women: 5.6 and 6.3 years, respectively), compared to high vegetables and fruit with low processed meat intake. Less pronounced and less consistent associations were observed for whole grains and fiber and for red meat. The findings provide initial evidence toward refining existing cancer prevention recommendations to optimize the intake and combination of foods in the general adult population.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".