Effect of estrogen and progesterone therapy on intraocular pressure: a systematic review and meta-analysis study
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to assess the effect of Hormone Replacement Therapy (HRT) with estrogen and progesterone on Intraocular Pressure (IOP) in postmenopausal women, with the objective of determining whether HRT can lower IOP and potentially reduce glaucoma risk. Following PRISMA guidelines, a comprehensive search was conducted up to June 2024. Eligible studies included randomized controlled trials and observational studies that reported IOP changes in postmenopausal women undergoing HRT. The pooled mean differences in IOP were calculated using both random-effects and fixed-effect models. The meta-analysis included 9 studies with a total of 1,024 participants. The pooled analysis showed a significant reduction in IOP among women receiving HRT compared to controls, with a mean difference of 3.84 mmHg (95% CI: 2.26 to 5.41, p < 0.01) in the random-effects model, and 2.36 mmHg (95% CI: 2.08 to 2.64, p < 0.01) in the fixed-effect model. Despite these significant results, there was high heterogeneity across studies (I² = 97%), likely due to variations in hormone types, dosages, and treatment durations. HRT is associated with a significant decrease in IOP in postmenopausal women, potentially offering protective benefits against glaucoma, although further research is needed to address the observed variability.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".