Metformin as a risk factor of pathogenesis of colorectal cancer in type 2 diabetes patients: a Meta-analysis
Bibliographic record
Abstract
Objective To evaluate the association between metformin therapy and the risk of pathogenesis of colorectal cancer in patients with type 2 diabetes. Methods PubMed, Embase, Medline, the Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Digital Journal Full-text Database, and database for Chinese Technical Periodicals (VIP) till Nov. 2014 were searched for the cohort studies and case-control studies in evaluating the effect of metformin on colorectal cancer. A meta-analysis was performed using Rev Man5.2 software. The quality assessment of included studies was evaluated according to the Newcastle-Ottawa scale (NOS). Results A total of 11 original articles met the inclusion criteria, 8 of them were articles withcohort studies including 12 subgroup studies with 447 234 subjects, and the other 3 case-control studies with 24 032 subjects. Metaanalysis of cohort studies showed that metformin therapy reduced the risk of pathogenesis of colorectal cancer by 30% in type 2 diabetes patients compared with those with other oral glucose-lowering drugs (RR=0.70, 95%CI: 0.58-0.85, P=0.0002). The results from the case-control studies showed that metformin therapy may be associated with a reduced risk of colorectal cancer in patients with type 2 diabetes (OR=0.78, 95%CI: 0.71-0.86, P<0.00001). Conclusion Metformin therapy can lower the risk of pathogenesis of colorectal cancer in patients with type 2 diabetes. DOI: 10.11855/j.issn.0577-7402.2015.07.14
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.046 |
| 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".