Prevention of Liver Fibrosis and Hepatocellular Carcinoma Using Antiplatelet Drugs
Bibliographic record
Abstract
GOAL: The aim of our research was to compile and analyze all existing observational data through a meta-analysis, evaluating the relationship between antiplatelet drugs, such as aspirin and clopidogrel, and the risks of liver fibrosis, portal vein thrombosis (PVT), and hepatocellular carcinoma (HCC). BACKGROUND: The association between antiplatelet drug use, especially the use of agents other than aspirin, and liver fibrosis, PVT, and HCC in patients with liver disease remains unclear. STUDY: Cochrane Library, Web of Science, EMBASE, and PubMed were searched for all records from their inception through Jul. 20, 2024. Per the defined inclusion and exclusion criteria, we carried out literature screening and data extraction. Following that, the quality of these studies was appraised with the Newcastle-Ottawa Scale. The primary outcomes were liver fibrosis, HCC, and PVT. Statistical analysis was conducted using Stata 17. RESULTS: The final analysis included 29 studies with 13,000 patients. Pooled results showed the HCC incidence after antiplatelet drug treatment was 3.6% (95% CI: 2.4%, 5.2%). The incidence of PVT after antiplatelet drug treatment was 48.6% (95% CI: 29.8%, 67.8%). Compared with the group not using antiplatelet drugs, the risk of liver fibrosis [hazard ratio (HR): 0.65, 95% CI: 0.56, 0.77; P<0.001] and the risk of HCC (HR: 0.63, 95% CI: 0.54, 0.73; P<0.001) were notably reduced in the group using antiplatelet drugs. CONCLUSIONS: The use of antiplatelet drugs may help prevent liver fibrosis, PVT, and HCC. Owing to the constraints of existing evidence, high-quality randomized controlled studies are essential to further corroborate these findings.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".