Association of Risperidone With Gastric Cancer: Triangulation Method From Cell Study, Animal Study, and Cohort Study
Bibliographic record
Abstract
Purpose: To examine the effects of risperidone, an atypical antipsychotic agent, on gastric cancer. Methods: A triangulation method comprising bench studies, including cell and animal experiments, and a retrospective cohort study, was subsequently performed. Results: The bench study indicated that risperidone exerted more prominent tumor inhibition effects than other atypical antipsychotics on the proliferation of KATO-III cells, a human gastric cancer cell line. Significant and dose-dependent cell viability was observed in Hs27 cells (control cells) in the presence of risperidone compared with in KATO-III cells. Both in vivo and in vitro results indicated that risperidone significantly inhibited the proliferation of KATO-III cells by inducing ROS and apoptosis, and that it suppressed the growth of xenografted KATO-III tumors in nude mice. In addition, the population-based cohort study found that risperidone users had reduced risks of gastric cancer compared with non-users, with lowered adjusted hazard ratios (HRs) for two induction periods (HR = 0.75; 95% confidence interval [CI] 0.68–0.83 for the one-year induction period, and HR = 0.68; 95% CI 0.61–0.75 for the two-year induction period). Conclusion: The findings are consistent with anticancer effects associated with risperidone, but further research and evaluations are warranted.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".