Impact of postdiagnostic statin use on ovarian cancer mortality: A systematic review and meta‐analysis of observational studies
Bibliographic record
Abstract
Aims To comprehensively evaluate the association between postdiagnostic statin use and mortality of ovarian cancer (OC) patients. Methods Using a comprehensive strategy, multiple databases (Medline, Embase and Web of Science) were systematically searched to identify observational studies that examined the correlation between statin use and OC mortality up to 31 December 2017. The studies were independently reviewed and selected based on predetermined selection criteria. Data were extracted independently and in duplicate. The risk of bias was evaluated with the Newcastle–Ottawa scale. Hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality were summarized with a profile likelihood random effects model. Results Of 269 studies, eight cohort studies of 19 904 OC patients met the inclusion criteria. Postdiagnostic statin use was inversely associated with all‐cause mortality/overall survival (summarized HR = 0.74; 95%CI = 0.63–0.87; I2 = 55%; n = 7) and cancer‐specific mortality (summarized HR = 0.87; 95%CI = 0.80–0.95; I2 = 0%; n = 3) of OC patients. These findings were consistent by subgroup analyses stratified by study and patient characteristics as well as adjustments made for potential confounders. A meta‐regression analysis found no effect of modification by these variables. Of note, similar significant inverse effects were also observed when increasing the intensity (highest vs. lowest) of postdiagnostic statin use (summarized HR = 0.84; 95%CI = 0.74–0.96; I2 = 0%; n = 3). Conclusion Postdiagnostic statin use can improve the survival of patients with OC. Further prospective cohort and randomized controlled trials are warranted to confirm the therapeutic role of statin use on the outcome of OC.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".