Cancer incidence attributable to red and processed meat consumption in Alberta in 2012
Bibliographic record
Abstract
BACKGROUND: Consumption of red and processed meats has been associated with an increased risk of colorectal cancer. The purpose of this study was to estimate the proportion and absolute number of cancers in Alberta in 2012 that could be attributed to the consumption of red and processed meat. METHODS: The number and proportion of colorectal cancers in Alberta that were attributable to red and processed meat consumption were estimated using population attributable risk. Relative risks were obtained from the World Cancer Research Fund's 2011 Continuous Update Project on Colorectal Cancer, and the prevalence of red and processed meat consumption was estimated using dietary data from Alberta's Tomorrow Project. Age- and sex-specific colorectal cancer incidence data for 2012 were obtained from the Alberta Cancer Registry. RESULTS: Among participants in Alberta's Tomorrow Project, 41%-61% of men and 14%-25% of women consumed more than 500 g of red and processed meat per week, which exceeds World Cancer Research Fund cancer prevention guidelines. For red meat consumption, population attributable risks for colorectal cancer were substantially higher for men (13.6%-17.9%) than for women (1.6%-2.1%). For processed meat consumption, the population attributable risks were also higher for men (3.2%-4.8%) than for women (1.5%-2.1%). Overall, about 12% of colorectal cancers, or 1.5% of all cancers, in Alberta in 2012 were attributable to the consumption of red and processed meat. INTERPRETATION: Red and processed meat consumption is estimated to acount for about 12% of colorectal cancers in Alberta. Decreasing its consumption has the potential to reduce to Alberta's cancer burden.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".