Statins Use and Risk of Breast Cancer Recurrence and Death: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
PURPOSE: Statins are widely prescribed drugs for lowering cholesterol. Some studies have suggested that statins can prevent breast cancer recurrence and reduce mortality rate. However they are not conclusive. Present systematic review and meta-analysis of published cohort studies was conducted to determine the effects of statins intake and risk of breast cancer recurrence and mortality rate. METHODS: Online databases (PubMed, Embase, Scopus, EBSCO and Cochrane Collaboration) were searched through October 2014. Pooled relative risks and 95 % confidence intervals were calculated with random-effects. RESULTS: A total of 8 cohort studies (4 for recurrence 2 for mortality and 2 for both) involving 124669 participants with breast cancer were eligible. Our results suggest a significant reduction in recurrence (OR= 0.79. I2= 38%) and death (OR = 0.84, I2 = 8.58 %) among statin users. CONCLUSION: Our meta-analysis suggests that breast cancer patients will benefit from statin intake, however from these cohorts we are unable to differentiate between various statins in terms of effectiveness and duration of use. We highly propose conducting randomized clinical trials.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".