Metformin and reduced risk of hepatocellular carcinoma in diabetic patients: a meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Recent epidemiological studies suggest that metformin treatment may reduce the risks of cancer and overall cancer mortality among patients with diabetes mellitus (DM). However, data on hepatocellular carcinoma (HCC) are very limited and inconsistent. This meta-analysis was designed to pool data currently available to determine the association between metformin use and HCC among diabetic patients. METHODS: The Medline and Embase databases were searched to identify the relevant studies between January 1966 and December 2011. The overall analysis was derived using a random-effects meta-analysis model (DerSimonian and Laird method). Subgroup analysis was performed to explore the source of heterogeneity and validate the results from overall analysis. The Newcastle-Ottawa Quality assessment scales were adopted for quality assessment; Begg's funnel plot and Egger's regression asymmetry test were used to detect the publication bias. RESULTS: A total of seven studies were identified, including three cohort studies and four case-control studies. Based on the available data, the overall prevalence of HCC was 3.40% (562/16,549) in DM patients. The overall analysis showed a significantly reduced risk of HCC in metformin users versus nonusers in diabetic patients (relative risk (RR) 0.24, 95% confidence interval (CI) 0.13-0.46, p < 0.001). Fifteen subgroup analyses were performed, and most of them (12/15 = 80%) provided supporting evidence for the results of overall analysis. Begg's (Z = -0.15, p = 0.8819) and Egger's test (t = -0.79, p = 0.468) showed no significant risk of having a publication bias. CONCLUSION: Metformin treatment was associated with reduced risk of HCC in diabetic patients. To clarify this relationship, more high-quality studies are required.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| 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".