Body Mass Index as a Risk Factor for Diffuse Large B-Cell Lymphoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Given the increasing prevalence of obesity, as measured by the Body Mass Index (BMI), and the significant impact of Diffuse Large B-cell lymphoma (DLBCL) on global health, it is crucial to update our understanding of the relationship between BMI and DLBCL. Objective: to carry out a systematic review (SR) with meta-analysis to determine the association between DLBCL and BMI Methods: This study is a systematic review and meta-analysis following the PRISMA guidelines. It searched PubMed/Medline, SCOPUS, Web of Science, and EMBASE. The inclusion criteria were primary observational studies involving adults with DLBCL confirmed by biopsy. Exclusions were non-peer-reviewed materials and studies without inferential statistics. The findings of the study are presented as association measures such as relative risks (RR), accompanied by their corresponding 95% confidence intervals (95% CI). Results: From 451 articles, 13 met the criteria for inclusion. The included studies, conducted between 2002 and 2013 in various countries, showed varied follow-up periods and sample sizes. The meta-analysis revealed that individuals with high BMI have a 1.31 times higher risk of developing DLBCL than those with lower BMI (RR: 1.31; 95% CI 1.07, 1.61). The heterogeneity among studies was moderate. Conclusions: The study confirms an association between higher BMI and the increased risk of developing DLBCL. This finding underscores the need to explore further how obesity, chronic inflammation, and the development and progression of DLBCL are interconnected. Understanding this area could significantly reduce DLBCL incidence and improve patient outcomes.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".