Metformin in the prevention of hepatocellular carcinoma in diabetic patients: A systematic review
Bibliographic record
Abstract
.: Preventive effect of metformin in hepatocellular carcinoma (HCC) is not entirely clear. We aimed to evaluate the use of metformin as a protective factor of HCC in diabetic patients. .: We carried out an electronic search on PUBMED/MEDLINE, Web of Science and LILACS databases, with no limit of date, from April 2017 to January 2019. Eligible studies included cohort and case-control studies. We adressed data about the use of metformin on the risk of HCC development. Two independent reviewers extracted the data. We evaluated the quality of studies by using the Newcastle-Ottawa scale and carried out a meta-analysis using random-effects models. .: The electronic searches identified 747 studies. After reading abstracts and titles, we excluded 327 duplicated papers and 383 irrelevant references. Eight studies were selected; four case-control and four cohort studies. All studies have observed that the therapy with metformin was associated with a lower risk of HCC, compared with non-metformin therapy. Five articles reported that patients treated with insulin, or insulin secretagogues, presented increased risk of HCC compared to those treated with metformin. One study found that not only statin but also aspirin reduced the risk of HCC, if combined with metformin. A meta-analysis, using the case-control studies, found a combined Odds Ratio of 0.468; 95% CI 0.275-0.799 for the association between HCC and the use of metformin. .: The use of metformin was associated with a reduced risk of HCC, and it may be a relevant factor for preventing HCC in diabetic patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".