Association of Menopausal Hormone Therapy with Risk of Pancreatic Cancer: A Systematic Review and Meta-analysis of Cohort Studies
Bibliographic record
Abstract
BACKGROUND: Although menopausal hormone therapy (MHT) is commonly prescribed, little is known about the association between MHT use and risk of pancreatic cancer. METHODS: We searched PubMed, Embase, and Cochrane Library, from inception until April 20, 2022. The risk of bias was assessed with the Newcastle-Ottawa Quality Assessment Scale. Pooled relative risks (RR) for pancreatic cancer risk were calculated using random-effects models. We computed prediction intervals (PI) and performed subgroup meta-analyses. Meta-regression was performed to investigate the sources of heterogeneity. RESULTS: This study included 2,712,313 women from 11 cohort studies. There was no association between MHT and pancreatic cancer risk (RR, 0.92; 95% confidence interval (CI), 0.83-1.02; I2, 64%; 95% PI, 0.68-1.25). Subgroup meta-analyses of four studies stratified by MHT formulations showed inverse associations with the risk of pancreatic cancer (women receiving estrogen-only MHT: RR, 0.77; 95% CI, 0.64-0.94; I2, 57%; estrogen plus progestin MHT: RR, 0.85; 95% CI, 0.75-0.96; I2, 0%). Subgroup analysis defined by recency and duration of treatment did not reveal evidence of associations between MHT and pancreatic cancer risk. CONCLUSIONS: This study found no association between the overall use of MHT and risk of pancreatic cancer. However, among four studies with data on MHT formulations, subgroup analysis showed a decreased risk of pancreatic cancer among users of estrogen-only and combined estrogen-progestin therapy. Owing to the inconsistent findings between our main and subgroup analyses, future studies stratified by MHT formulations are warranted. IMPACT: The findings of this study indicate that future investigation should focus on MHT formulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".