Prolactin-Elevating Antipsychotics and the Risk of Endometrial Cancer
Bibliographic record
Abstract
BACKGROUND: The use of antipsychotics may increase the risk of endometrial cancer through elevation of prolactin levels. We investigated the association between antipsychotics that are known to cause prolactin elevation and the risk of endometrial cancer. METHODS: In data from the United Kingdom Clinical Practice Research Datalink, all women who were newly treated with antipsychotics from 1990-2013 were identified and followed until 2014. Within this cohort of antipsychotic users, a nested case-control analysis was conducted. Main exposure was nonsporadic use of prolactin-elevating antipsychotics, and the active comparator was prolactin-sparing antipsychotics. Cases were women newly diagnosed with endometrial cancer (ICD-10) matched with up to 20 controls on age, calendar year of cohort entry, linkability to the Hospital Episode Statistics repository, and duration of follow-up. Conditional logistic regression models were used to determine the association of prolactin-elevating antipsychotics and endometrial cancer compared with prolactin-sparing antipsychotics. All analyses were adjusted for relevant potential confounders, including smoking, obesity, and diabetes mellitus. RESULTS: The cohort included 65,930 women. During 366,112 person-years of follow-up, there were 139 cases of endometrial cancer (incidence rate: 38/100,000 person-years), which were matched to 1,603 controls. Compared with the use of prolactin-sparing antipsychotics, the use of prolactin-elevating antipsychotics was not associated with an increased risk of endometrial cancer (adjusted odds ratio [aOR] = 1.00; 95% CI, 0.68-1.48). These findings remained similar with different durations of use (≤ 1 year, aOR = 1.07; 95% CI, 0.64-1.78, and > 1 year, aOR = 0.95; 95% CI, 0.58-1.54) and were robust to various sensitivity analyses. CONCLUSIONS: Prolactin-elevating antipsychotics were not associated with an increased risk of 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 | 0.001 | 0.005 |
| 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 | 0.000 |
| 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".