The impact of statins on melanoma survival: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it