Red and Processed Meat Consumption Increases Risk for Non-Hodgkin Lymphoma
Bibliographic record
Abstract
The association between consumption of red and processed meat and non-Hodgkin lymphoma (NHL) remains unclear. We performed a meta-analysis of the published observational studies to explore this relationship.We searched databases in MEDLINE and EMBASE to identify observational studies which evaluated the association between consumption of red and processed meat and risk of NHL. Quality of included studies was evaluated using Newcastle-Ottawa Quality Assessment Scale (NOS). Random-effects models were used to calculate summary relative risk (SRR) and the corresponding 95% confidence interval (CI).We identified a total of 16 case-control and 4 prospective cohort studies, including 15,189 subjects with NHL. The SRR of NHL comparing the highest and lowest categories were 1.32 (95% CI: 1.12-1.55) for red meat and 1.17 (95% CI: 1.07-1.29) for processed meat intake. Stratified analysis indicated that a statistically significant risk association between consumption of red and processed meat and NHL risk was observed in case-control studies, but not in cohort studies. The SRR was 1.11 (95% CI: 1.04-1.18) for per 100 g/day increment in red meat intake and 1.28 (95% CI: 1.08-1.53) for per 50 g/day increment in processed meat intake. There was evidence of a nonlinear association for intake of processed meat, but not for intake of red meat.Findings from our meta-analysis indicate that consumption of red and processed meat may be related to NHL risk. More prospective epidemiological studies that control for important confounders and focus on the NHL risk related with different levels of meat consumption are required to clarify this association.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.025 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".