Towards refining World Cancer Research Fund/American Institute for Cancer Research cancer prevention recommendations for red and processed meat intake: insights from Alberta’s Tomorrow Project cohort
Bibliographic record
Abstract
Current cancer prevention recommendations advise limiting red meat intake to <500 g/week and avoiding consumption of processed meat, but do not differentiate the source of processed meat. We examined the associations of processed meat derived from red v. non-red meats with cancer risk in a prospective cohort of 26 218 adults who reported dietary intake using the Canadian Diet History Questionnaire. Incidence of cancer was obtained through data linkage with Alberta Cancer Registry with median follow-up of 13·3 (interquartile range (IQR) 5·1) years. Multivariable Cox proportional hazards regression models were adjusted for covariates and stratified by age and sex. The median consumption (g/week) of red meat, processed meat from red meat and processed meat from non-red meat was 267·9 (IQR 269·9), 53·6 (IQR 83·3) and 11·9 (IQR 31·8), respectively. High intakes (4th Quartile) of processed meat from red meat were associated with increased risk of gastrointestinal cancer adjusted hazard ratio (AHR): 1·68 (95 % CI 1·09, 2·57) and colorectal cancers AHR: 1·90 (95 % CI 1·12, 3·22), respectively, in women. No statistically significant associations were observed for intakes of red meat or processed meat from non-red meat. Results suggest that the carcinogenic effect associated with processed meat intake may be limited to processed meat derived from red meats. The findings provide preliminary evidence towards refining cancer prevention recommendations for red and processed meat intake.
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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 | 0.053 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".