Metformin use and Occurrence of Hepatocellular Carcinoma in Patients with Type II Diabetes Mellitus
Bibliographic record
Abstract
Objectives: There is not much clarity on the metformin's preventive effect in hepatocellular carcinoma (HCC). The aim of this study was to determine the association between metformin use and HCC. Materials and Methods: An electronic search was carried on Web of Science, Scopus, and PubMed/MEDLINE, from January 2010 to January 2022, for 12 years. Case–control and cohort studies were part of the eligible studies. Data were extracted by two independent reviewers. The Newcastle–Ottawa scale was used to check the quality of studies. Results: A total of 928 (872 + 56) studies were identified in our search, among which a total of 623 articles were analyzed after removing the duplicates. After the retrieved papers were analyzed for their titles and abstracts, a total of 575 articles were excluded on the basis of inclusion criteria, respectively. Forty-eight full-text articles were assessed for final data extraction, of which 11 articles were selected. The pooled analysis of included studies showed a combined odds ratio of 0.87; 95% confidence interval 0.86–0.89 for the association between HCC and the use of metformin. It was noticed that all the studies found nonmetformin therapy to entail a higher risk of HCC in comparison to therapy with metformin with the funnel plot showing asymmetric distribution, with Egger's test showing P < 0001. Conclusions: Metformin use reduces the HCC development risk, and therefore, it may be used in diabetics for the prevention of HCC.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".