Antidepressants and Risk of Liver Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background:Previous results regarding the association between the antidepressants use and risk of liver cancer are controversial.Objective:This study aimed to assess whether antidepressants use increases liver cancer risk.Methods:We systematically searched several English and Chinese databases, including the Cochrane Library, MEDLINE, Embase, PsycINFO, Web of Science, CNKI, CQVIP database, Wanfang database, and SinoMed, and 3 clinical trial registration platforms through May 2022. Observational studies evaluating liver cancer risk in patients on antidepressants use were included, and the quality of studies was assessed using the Newcastle-Ottawa scale. A random-effects model was used to calculate the pooled effect estimates and 95% confidence intervals (CIs).Results:We included 11 studies with a total of 132 396 liver cancer cases. The meta-relative risk (RR) for liver cancer associated with antidepressants use was 0.72 (95% CI 0.59-0.86). In subgroup analyses, only selective serotonin reuptake inhibitors were negatively correlated with risk of liver cancer (RR 0.64, 95% CI 0.51-0.79); both dose subgroups ≤365cDDD (RR 0.77, 95% CI 0.69-0.85) and >365cDDD (RR 0.57, 95% CI 0.40-0.81) were associated with lower liver cancer risk; only in patients with chronic viral hepatitis, the use of antidepressants reduced liver cancer risk (RR 0.70, 95% CI 0.54-0.90).Conclusions and Relevance:The result of the current meta-analysis shows antidepressants use is not associated with increased risk of liver cancer and appears to be correlated with decreased risk. However, the observed association needs to be verified by more powerful evidence from prospective, methodologically rigorous studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".