The impact of statins on melanoma survival: a systematic review and meta-analysis
Bibliographic record
Abstract
Statin use may decrease recurrence and improve survival in patients with melanoma. In this systematic review and meta-analysis, we examine the current body of literature concerning the use of statins as an adjunctive therapy in melanoma, Medline, EMBASE, CENTRAL, and PubMed were systematically searched from inception through to April 2023. Studies were included if they compared patients with melanoma receiving and not receiving statin therapy concurrently with their oncologic treatment in terms of long-term oncologic outcomes. The primary outcome was 5-year overall survival (OS). Meta-analyses was performed with DerSimonian and Laird random effects. Risk of bias was assessed with the ROBINS-I and GRADE was used to assess certainty of evidence. From 952 citations, eight non-randomized studies were identified. Included studies were conducted between 2007 and 2022. Random effects meta-analysis of adjusted hazard ratios from three studies suggested an improvement in 5-year OS with statin use with wide 95% confidence intervals (CIs) crossing the line of no effect (hazard ratio 0.87, 95% CI: 0.73-1.04, P = 0.12, I2 = 95%, very-low certainty). Outcome reporting was heterogeneous across all other oncologic outcomes such that pooling of data was not possible. Risk of bias was serious for seven studies and moderate for one study. This systematic review of studies evaluating the impact of statin use on survival in patients with melanoma found a 13% reduction in risk of death at 5 years from diagnosis - a point estimate suggesting benefit. However, the wide 95% CIs and resultant type II error risk create significant uncertainty.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.042 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".