Comprehensive Review of Red Meat Consumption and the Risk of Cancer
Bibliographic record
Abstract
Red and processed meat consumption rates are increasing in the United States. In this review, we present the current evidence that links red meat consumption and cancer development. A literature search was conducted in the PubMed and Google Scholar databases to review red meat consumption and its association with breast cancer and gastrointestinal cancer. Due to the presence of heme iron, which triggers oxidative reactions that eventually result in tumor formation, red meat consumption is strongly associated with the development of breast cancer. Ingestion of red meat increases Helicobacter pylori infections, resulting in enhanced expression of the CagA gene and the secretion of pro-inflammatory cytokines. This is the leading cause of gastric cancer. There is a strong correlation between heterocyclic amines and polycyclic aromatic hydrocarbons in red meat and the development of pancreatic cancer. However, additional research is necessary to confirm this finding. Adult colorectal cancer is caused by the formation of heterocyclic amines and DNA adducts due to the intake of red and processed meats cooked at higher temperatures. The consumption of poultry is associated with a reduced risk of breast and gastrointestinal cancers, but the results are inconsistent. The evidence is strong for the association between red meat and breast cancer and most gastric cancers. The presence of aromatic hydrocarbons, heterocyclic amines, and heme iron in red meat has been found to be behind tumorigenesis. Poultry has been shown to have a low association with cancer, but additional research is needed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".