The Association between Statin Medication and Progression after Surgery for Localized Renal Cell Carcinoma
Bibliographic record
Abstract
PURPOSE: Evidence suggests that statins may influence pathways of renal cell carcinoma proliferation, although to our knowledge no study has examined the influence of statin medications on the progression of renal cell carcinoma in humans. MATERIALS AND METHODS: We identified 2,608 patients with localized renal cell carcinoma who were treated surgically between 1995 and 2010 at our tertiary referral center. Competing risks Cox proportional hazards models were used to evaluate the relationship between statin use and time to local recurrence or progression (metastases or death from renal cell carcinoma) and overall survival. Statin use was modeled as a time dependent covariate as a sensitivity analysis. Models were adjusted for clinical and demographic features. RESULTS: Of 2,608 patients 699 (27%) were statin users at surgery. Statin users had similar pathological characteristics compared to nonusers. At a median followup of 36 months there were 247 progression events. Statin use was associated with a 33% reduction in the risk of progression after surgery (HR 0.67, 95% CI 0.47-0.96, p = 0.028) and an 11% reduction in overall mortality that was not significant (HR 0.89, 95% CI 0.71-1.13, p = 0.3). Modeling statin use as a time dependent covariate attenuated the risk reduction in progression to 23% (HR 0.77, p = 0.12) and augmented the risk reduction in overall survival (HR 0.71, p = 0.002). CONCLUSIONS: In our cohort statin use was associated with a reduced risk of progression and overall mortality, although this effect was sensitive to the method of analysis. If validated in other cohorts, this finding warrants consideration of prospective research on statins in the adjuvant setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".