Antipsychotic use is inversely associated with gastric cancer risk: A nationwide population‐based nested case‐control study
Bibliographic record
Abstract
OBJECTIVE: The association between antipsychotic use and gastric cancer risk remains unclear. Therefore, this study aimed to determine the association between antipsychotic exposure and the incidence of gastric cancer. METHODS: Using a nested case-control design, a total of 34 470 gastric cancer patients and 163 430 nongastric cancer controls were identified from Taiwan's National Health Insurance Research Database between 1 January 1997 and 31 December 2013. We analyzed the data using a conditional logistic regression model to adjust for possible confounding variables. RESULTS: Antipsychotic use was independently inversely associated with gastric cancer risk after controlling for potential confounding factors including income, urbanization, medications, physical and medical illness, aspirin use, nonsteroidal anti-inflammatory drug use and triple therapy. In addition, dose-dependent trends against gastric cancer risk were also shown with individual antipsychotic compounds including thioridazine, haloperidol, sulpiride, clozapine, olanzapine, quetiapine, amisulpride, and risperidone. A sensitivity analysis showed that second-generation antipsychotics had significant dose-dependent effects in reducing the risk of gastric cancer risk in patients with and without peptic ulcer disease. CONCLUSIONS: Antipsychotic use was inversely associated with gastric cancer risk, and dose-dependent effects against gastric cancer were also seen with several individual antipsychotic compounds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".