The Association between Red Meat Consumption and Advanced Colorectal Adenomas in a Population Undergoing a Screening-Related Colonoscopy in Alberta, Canada
Bibliographic record
Abstract
The association between red meat consumption and colorectal cancer has been rigorously examined. However, a more comprehensive understanding of how the intake of unprocessed red meat contributes to the development of early precancerous colorectal lesions, such as advanced colorectal adenomas (ACRAs), requires further investigation. We examined the associations between different types of red meat intake and ACRAs in a sample population of 1083 individuals aged ≥ 50 years undergoing an initial screening colonoscopy in Calgary, Alberta, Canada. Associations between grams per day of total, processed, and unprocessed red meat from diet history questionnaires and ACRAs were evaluated with multivariable logistic regression models. We also applied cubic spline models fitted with three knots (10th, 50th, and 90th percentiles) to identify potential nonlinear associations. We did not observe a meaningful association between unprocessed red meat intake and the presence of ACRAs. In contrast, for every 10 g/d increase in total and processed meat intake, we observed an increase in the odds of ACRAs at the screening colonoscopy (adjusted odds ratio (OR) = 1.05, 95% [CI = 1.01–1.09], p = 0.04) and (adjusted OR = 1.11, 95% [CI = 1.02–1.20], p = 0.02), respectively. This study highlights the importance of differentiating between types of red meat consumption in the context of dietary risks associated with ACRAs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".