Effect of metformin on the prognosis of diabetic patients combined with gynecologic cancer: A Meta-analysis
Bibliographic record
Abstract
Objective To systematically evaluate the effect of metformin on the prognosis of diabetic patients combined with gynecologic cancer. Methods The database including PubMed, Embase, CNKI and Wangfang, were electronically searched with no language restriction from their inception to March 2017 to collect the studies about the effect of metformin on the prognosis of diabetic patients combined with gynecologic cancer. The references in reviews were also searched. According to the inclusion and exclusion criteria, two reviewers screened the literatures independently, extracted data and assessed methodological quality by the Newcastle-Ottawa scale. The primary end points included overall survival (OS) and progress free survival (PFS). The outcome measures were the pooled hazard ratios (HR) and 95% confidence intervals (95% CI). I2 was performed in a heterogeneity assessment. Publication bias was evaluated by using Begg's funnel plot and Egger's test, and the sensitivity analysis was conducted to confirm robustness. The Meta-analysis was performed using STATA 12.0 software. Results Sixteen eligible retrospective cohort studies were included and the score of quality assessment were ranged from 6 to 9. The Meta-analysis showed that metformin could improve the OS of diabetic patients with gynecologic tumors (HR=0.71, 95%CI 0.59-0.85, P=0.000). Subgroup analysis revealed that metformin could improve the OS of diabetic patients combined with endometrial cancer (HR=0.70, 95%CI 0.54-0.89, P=0.004) and diabetic patients combined with cervical cancer (HR=0.95, 95%CI 0.90-1.00, P=0.048). Meanwhile metformin improved the OS (HR=0.56, 95%CI 0.38-0.83, P=0.004) and PFS (HR=0.45, 95%CI 0.30-0.68, P=0.000) of diabetic patients with ovarian cancer after adjusting for confounders. Conclusions The use of metformin is positive for the prognosis of diabetic patients combined with gynecologic cancer. It may improve the OS of diabetic patients with endometrial cancer and diabetic patients with cervical cancer. In addition, it improves the OS and PFS of diabetic patients with ovarian cancer. DOI: 10.11855/j.issn.0577-7402.2018.02.11
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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 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".