Impact of hormone replacement therapy on all-cause and cancer-specific mortality in colorectal cancer: A systematic review and dose‒response meta-analysis of observational studies
Bibliographic record
Abstract
Abstract Background The effect of hormone replacement therapy (HRT) on colorectal cancer (CRC) mortality and all-cause mortality remains unclear. We conducted a systematic review and dose‒response meta-analysis to determine the effects of HRT on CRC mortality and all-cause mortality. Methods We searched the PubMed, Embase, and Cochrane Library electronic databases for all relevant studies published until June 2022 to investigate the effects of HRT exposure on survival rates for patients with CRC. Two reviewers independently extracted individual study data and evaluated THE risk of bias among the studies using the Newcastle‒Ottawa Scale. To examine a potential nonlinear relationship between the year of HRT use and CRC mortality, we performed a two-stage random effects dose‒response meta-analysis. RESULTS Ten cohort studies encompassing 480,628 individuals were included. The meta-analysis revealed that HRT was inversely associated with the risk of CRC mortality [hazard ratios (HR) = 0.77, 95% CI (0.68, 0.87), I2 = 69.5%, P < 0.05]. Pooled results from seven cohort studies revealed a significant association between HRT and the risk of all-cause mortality [HR = 0.71, 95% CI (0.54, 0.92), I2 = 89.6%, p < 0.05]. A linear (P for nonlinearity = 0.34) dose‒response analysis showed a 3% decrease in the risk of CRC for each additional year of HRT use; this decrease was significant [HR = 0.97, 95% CI(0.94, 0.99), P < 0.05]. An additional linear (P for nonlinearity = 0.88) dose‒response analysis showed a nonsignificantly decrease in the risk of all-cause mortality for each additional year of HRT use. CONCLUSIONS This study suggests that the use of HRT is inversely associated with all-cause and colorectal cancer mortality, thus causing a significant decrease in mortality rates over time. Further studies are warranted to confirm this association.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".