Endometrial cancer and antidepressants
Bibliographic record
Abstract
To our knowledge, the association between antidepressant exposure and endometrial cancer has not been previously explored. Herein, we aim to investigate the association between antidepressant prescription, including novel antidepressants, and the risk for endometrial cancer in a population-based study.Data for the analysis were derived from National Health Insurance Research Database. We identified 8392 cases with a diagnosis of endometrial cancer and 82,432 matched controls. A conditional logistic regression model was used, with adjusting for potentially confounding variables (e.g., comorbid psychiatric diseases, comorbid physical diseases, and other medications). Risk for endometrial cancer in the population-based study sample was categorized by, and assessed as a function of, antidepressant prescription and cumulative dosage.We report no association between endometrial cancer incidence and antidepressant prescription, including those prescribed either selective serotonin reuptake inhibitors (adjusted odds ratio [OR] = 0.98; 95% confidence interval [CI], 0.84-1.15) or serotonin norepinephrine reuptake inhibitors (adjusted OR = 1.14; 95% CI, 0.76-1.71). We also did not identify an association between higher cumulative doses of antidepressant prescription and endometrial cancer.There was no association between antidepressant prescription and endometrial cancer.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".