Impact of statin on renal cell carcinoma patients undergoing nephrectomy. Does it affect cancer progression and improves survival? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Renal cell carcinoma (RCC) is regarded as one of the most common malignant tumors. Various concomitant medications in RCC patients undergoing surgery are investigated to explore the potential for improving survival and preventing disease recurrence, including statin. It has been observed that these drugs induce apoptosis, thereby inhibiting tumor growth and angiogenesis. We aimed to perform a systematic review and meta-analysis to enhance the level of evidence for statin in RCC. METHODS: A systematic literature search was conducted in several online databases, including PubMed, Scopus, and Sciencedirect, using terms relevant to the use of statins in RCC patients undergoing nephrectomy for publications published up to July 2023, according to a registered review procedure (CRD42023452318). The Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias of the included study. Review Manager 5.4 was used for all analyses. RESULTS: Seven articles was eligible for our study. The analysis revealed that patients receiving statin had a better overall survival compared to patients who does not receive statin (HR 0.71, 95% CI 0.51-0.97, p = 0.03, I2 = 76%). However, there was insignificant difference in terms of CSS, DFS, and PFS between RCC patients receiving statin and without statin. CONCLUSIONS: Statin has substantial benefits for improving OS. Even though the outcomes for CSS, DFS, and PFS were insignificant, the potential role of statins as a supplementary therapy in surgically treated RCC still requires further investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".