Does pravastatin promote cancer in elderly patients? A meta-analysis
Bibliographic record
Abstract
BACKGROUND: An increase in the incidence of cancer among elderly people assigned to pravastatin therapy has been reported in a randomized controlled trial; however, this finding has been attributed to chance. Our aim was to assess the effect of pravastatin therapy on cancer risk and to examine whether the effect varies according to age by performing a detailed meta-analysis and meta-regression analysis of randomized controlled trials. METHODS: We performed a comprehensive literature search for relevant studies published before February 2006. Before analysis, the selected studies were evaluated for publication bias and heterogeneity. Pooled relative risk estimates with 95% confidence intervals (CIs) were calculated using fixed-and random-effects models. Meta-regression analysis was performed to examine the impact of age on the study estimates of the relative risk of cancer due to pravastatin therapy. RESULTS: Twelve trials that investigated the use of pravastatin therapy for cardiovascular outcomes were included in the analysis (n = 42 902). Although the overall association between pravastatin use and cancer was not statistically significant in the fixed-effects (risk ratio [RR] 1.06, 95% CI 0.99 - 1.13) or random-effects model (RR 1.06, 95% CI 0.97 - 1.14), the meta-regression analysis showed that the age of study participants significantly modified the effect of pravastatin therapy on cancer risk (p = 0.006). Specifically, this analysis showed that pravastatin therapy was associated with an increasing risk of cancer as age increased. This finding was remarkably robust in the sensitivity analysis. INTERPRETATION: Our findings suggest an association between pravastatin therapy and cancer in elderly patients. However, given the importance of this potential association, further verification is warranted.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.056 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".