Meat, fish, and esophageal cancer risk: a systematic review and dose-response meta-analysis
Bibliographic record
Abstract
Risk factors for esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) are well defined, while the role of diet in these conditions remains controversial. To help elucidate the role of particular dietary components, major bibliographic databases were searched for published studies (1990-2011) on associations between esophageal cancer risk (EC) and consumption of various types of meat and fish. Random-effects models and dose-response meta-analyses were used to pool study results. Subgroup analyses were conducted by histological subtype, study design, and nationality. Four cohorts and 31 case-control studies were identified. The overall pooled relative risk (RR) of EC and the confidence intervals (CIs) for the groups with the highest versus the lowest levels of intake were as follows: 0.99 (95% CI: 0.85-1.15) for total meat; 1.40 (95%CI: 1.09-1.81) for red meat; 1.41 (95%CI: 1.13-1.76) for processed meat; 0.87 (95%CI: 0.60-1.24) for poultry; and 0.80 (95%CI: 0.64-1.00) for fish. People with the highest levels of red meat intake had a significantly increased risk of ESCC. Processed meat intake was associated with increased risk of EAC. These results suggest that low levels of red and processed meat consumption and higher levels of fish intake might reduce EC risk.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".