Metformin Improves Overall Survival of Colorectal Cancer Patients with Diabetes: A Meta-Analysis
Bibliographic record
Abstract
Introduction. Diabetic population has a higher risk of colorectal cancer (CRC) incidence and mortality than nondiabetics. The role of metformin in CRC prognosis is still controversial. The meta-analysis aims to investigate whether metformin improves the survival of diabetic CRC patients.Methods. PubMed, EMBASE, and Cochrane Library were searched till July 1, 2016. Cohort studies were included. All articles were evaluated by Newcastle-Ottawa Scale. Hazard Ratios (HRs) with 95% confidence intervals (CIs) for each study were calculated and pooled HRs with corresponding 95% CIs were generated using the random-effects model. Heterogeneity and publication bias were assessed.Results. We included seven cohort studies with a medium heterogeneity ( I2 = 56.1% and p=0.033 ) in our meta-analysis. An improved overall survival (OS) for metformin users over nonusers among colorectal cancers with diabetes was noted (HR 0.75; 95% CI 0.65 to 0.87). However, metformin reveals no benefits for cancer-specific survival (HR 0.79, 95%, CI 0.58 to 1.08).Conclusions. Metformin prolongs the OS of diabetic CRC patients, but it does not affect the CRC-specific survival. Metformin may be a good choice in treating CRC patients with diabetes mellitus in clinical settings.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.043 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".