New use for old drugs: The protective effect of atypical antipsychotics on hepatocellular carcinoma
Bibliographic record
Abstract
It has been encouraged to use large existing data like insurance claims data to investigate the new indications of old drugs. New strategies of research are warranted to identify feasible drugs. We conducted a dual research model with a population-based case-control study using Taiwan's National Health Insurance Research Database and an in vitro study to investigate the association between atypical antipsychotic and Hepatocellular carcinoma (HCC) risk. The study herein consists of two components. The first is a population-based case-control study using existing data from the Taiwan National Health Insurance Research Database. The second component was an in vitro study in which HCC cell lines (Huh7 and Hep G2) were treated with risperidone, quetiapine and clozapine. after treatment of the foregoing antipsychotics, the HCC cell lines were assessed for cell proliferation, invasion and apoptosis. Multivariate conditional logistic regression analysis revealed that antipsychotic use was independently and inversely associated with HCC risk (adjusted odds-ratio [aOR]:0.85, 95% CI: 0.81-0.89). The protective effect was dose-dependent: compared to the low cumulative defined daily dose (cDDD) group (0-29 cDDD), the 30-89 cDDD and ≥90 cDDD groups were associated with significantly reduced risk for HCC (aOR: 0.56, 95% CI: 0.41-0.76; aOR: 0.37, 95% CI: 0.27-0.50, respectively). In vitro study results indicated that risperidone, quetiapine and clozapine significantly inhibited cell proliferation, invasion and induced apoptosis in human HCC cell lines. Our results herein suggested that antipsychotic use might reduce the risk of HCC and may provide evidence for new uses of old drugs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".