Use of non‐steroidal anti‐inflammatory drugs and risk of non‐Hodgkin lymphoma: a systematic review and meta‐analysis
Bibliographic record
Abstract
Epidemiological study findings regarding the association between use of non-steroidal anti-inflammatory drugs (NSAIDs) and risk of non-Hodgkin lymphoma (NHL) have been inconsistent. We aimed to systematically review epidemiological studies of the association and calculate pooled relative risks using meta-analytic methods. We searched eight electronic literature databases and three clinical trial registers to identify all studies (including observational studies and randomized clinical trials) of the association published prior to October 2013. Identified studies were independently reviewed by two researchers. We used a random effects model to calculate pooled odds ratio (PORs). Heterogeneity amongst studies was examined using Cochran's Q and I-squared (I(2)) tests; and sources of heterogeneity were explored using subgroup and meta-regression analyses. A total of 17 studies (12 case-control studies and five cohort studies), all adult studies, were included. Use of NSAIDs was not associated with overall risk of NHL [POR = 1.05, and 95% confidence interval (95% CI) 0.90-1.22] or NHL subtypes including B-cell lymphoma, T-cell lymphoma, follicular lymphoma, diffuse large B-cell lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL). Aspirin use was associated with reduced risk of CLL/SLL (POR = 0.70, 95% CI 0.54-0.91) but not with the risk of all NHLs (POR = 1.02, 95% CI 0.89-1.17). Use of non-aspirin NSAIDs was associated with increased risk of NHL (POR = 1.41, 95% CI 1.01-1.97) amongst females only. The epidemiologic evidence remains inconclusive. Effects of NSAIDs may differ by drug type, NHL subtype, and sex and more studies taking into consideration these differences are needed.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".