The Effects of Vitamin D Supplementation on Biomarkers of Inflammation and Oxidative Stress Among Women with Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
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Bibliographic record
Abstract
Abstract The current systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to summarize the effect of vitamin D supplementation on biomarkers of inflammation and oxidative stress among women with polycystic ovary syndrome (PCOS). Cochrane library, Embase, PubMed, and Web of Science database were searched to identify related randomized-controlled articles (RCTs) published up to November 2017. Two researchers assessed study eligibility, extracted data, and evaluated risk of bias of included RCTs, independently. To check heterogeneity Q-test and I2 statistics were used. Data were pooled by using the random-effect model and standardized mean difference (SMD) was considered as summary effect size. Seven RCTs were included into our meta-analysis. The findings showed that vitamin D supplementation in women with PCOS significantly decreased high-sensitivity C-reactive protein (hs-CRP) (SMD −1.03; 95% CI, −1.58, −0.49; p <0.001) and malondialdehyde (MDA) (SMD −1.64, 95% CI −2.26 to −1.02, p <0.001), and significantly increased total antioxidant capacity (TAC) levels (SMD 0.86, 95% CI 0.08 to 1.64, p=0.03). Vitamin D supplementation had no significant effect on nitric oxide (NO) (SMD 0.11, 95% CI −0.44 to 0.66, p=0.69) and total glutathione (GSH) levels (SMD 0.54, 95% CI −0.20 to 1.28, p=0.15). Overall, the current meta-analysis demonstrated that vitamin D supplementation to women with PCOS resulted in an improvement in hs-CRP, MDA and TAC, but did not affect NO and GSH levels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.022 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it