Non-Hodgkin's lymphoma--meta-analyses of the effects of corticosteroids and non-steroidal anti-inflammatories
Bibliographic record
Abstract
OBJECTIVE: Recent research has focused on the effects of corticosteroids and non-steroidal anti-inflammatory drugs/agents (NSAIDs) on non-Hodgkin's lymphoma (NHL) risk, with inconclusive results. We conducted meta-analyses of data published to date, to ascertain the over-all association between NHL and corticosteroid use, and between NHL and NSAID use. METHODS: Literature searches were performed to find studies assessing the effects of corticosteroids and/or NSAIDs on NHL risk. We analysed nine case-control studies and one cohort study of the effect of corticosteroids and/or NSAIDs on NHL risk. We performed a formal meta-analysis using summary measures from these studies. RESULTS: The studies contributed 6897 NHL cases and 8881 controls for the corticosteroid analyses, and 5794 NHL cases and 34,707 controls for the NSAID analyses. There was no heterogeneity of the odds ratio (OR) estimates. The overall OR for the effect of corticosteroid exposure on NHL occurrence was not suggestive of an increased risk [OR 1.09, 95% confidence interval (CI) 0.96-1.24]. Similarly, the OR for the effect of NSAIDs on NHL occurrence did not support an increased risk (OR 0.93, 95% CI 0.74-1.14). CONCLUSIONS: Our meta-analyses suggest little evidence that corticosteroid or NSAID exposures are themselves risk factors for NHL. Early data linking corticosteroids and/or NSAIDs with NHL may reflect an underlying increased risk of lymphoma in patient populations that use these medications (i.e. autoimmune diseases such as rheumatoid arthritis), and may point to the importance of disease activity in driving NHL risk in these populations.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.067 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".