Associations between the consumption of red meat and processed meat and the incidence of colorectal cancer in Asia: a meta-analysis
Bibliographic record
Abstract
Objective This study aimed to evaluate the correlation between the consumption of red meat and processed meat and the incidence of colorectal cancer (CRC) in Asia and provide a scientific basis for reducing the incidence of CRC. Methods PubMed, Embase, Cochrane Library, and other databases were searched electronically to collect studies on the correlation between the consumption of red meat and processed meat and the incidence of CRC in Asia. After the quality evaluation of the Newcastle–Ottawa scale, meta-analyses of the selected studies were performed using RevMan 5.4.1. The odds ratio (OR) and 95% confidence interval (95% CI) were combined, and the heterogeneity among the included studies was analyzed via sensitivity analysis. I2 was used to evaluate the heterogeneity among the included studies. Results Twelve articles were included, which involved 13,292 and 12,544 cases in the case and control groups, respectively. The results of the meta-analysis revealed that in the study of the correlation between the consumption of red meat and the incidence of colon cancer, the combined OR was 2.14 (P < 0.00001); that for the consumption of red meat and the incidence of CRC, the OR was 1.77 (P = 0.006); that for the consumption of red meat and the incidence of rectal cancer, the OR was 2.42 (P = 0.0009); and that for the consumption of processed meat and the incidence of CRC, the combined OR was 1.51 (P > 0.05). Conclusion The results suggest that red meat is a risk factor for the incidence of colon, colorectal, and rectal cancers. However, no significant correlation was found between the consumption of processed meat and the incidence of CRC.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.064 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".