The effect of hormone replacement therapy on cervical cancer risk in perimenopausal women: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Background: Hormone replacement therapy (HRT) alleviates menopausal symptoms in perimenopausal women and help improve their quality of life, but its increased risk of cervical cancer (CC) remains to be evaluated. Methods: A system review and meta-analysis was conducted to retrieve literature related to HRT and CC risk by searching Pubmed, Embase, Science Direct, Web of Science, and Google Scholar databases. After screening the literature according to inclusion criteria and assessing the risk of bias using the Newcastle Ottawa Scale, the odd ratio (OR) values of HRT relative to CC were pooled. Results: A total of 9 articles were included in this study, including 5 cohort studies and 4 case control studies. The sample size of perimenopausal women in the literature ranged from 60 to 584,742. The overall quality of the literature was good. The meta-analysis results showed that HRT (current and persist use) had a reduced risk for CC (OR=0.70, 95% confidence interval (CI) [0.58, 0.85]), an increased risk for any cytological abnormality related to CC (OR=1.38, 95% CI [1.22, 1.55]), also an increased risk for adenocarcinoma of CC (OR=1.82, 95% CI [0.91,3.65]), but a decreased risk for squamous cell carcinoma of CC (OR=0.74, 95% CI [0.57, 0.96]). The subtype was a significant source of heterogeneity in this meta-analysis. Conclusion: HRT does not increase the overall risk of cervical cancer, but it increases the risk of cervical adenocarcinoma subtype and is associated with the risk of cancer-related cytological lesions.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| 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".