A comprehensive insight into effects of green tea extract in polycystic ovary syndrome: a systematic review
Bibliographic record
Abstract
BACKGROUND: Polycystic ovary syndrome (PCOS), as the most common endocrine disorder in reproductive-aged women, is characterized by oxidative stress and ovarian tissue inflammation. Green tea extract (GTE) potentially possesses therapeutic effects for PCOS because of the antioxidant and anti-inflammatory compounds. This systematic review evaluates the potential roles of GTE on metabolic variables, hormone levels, and ovarian function in PCOS. METHODS: A systematic review was conducted of published studies reporting the effects of GTE on PCOS. Several major databases, including PubMed, SCOPUS, and Google Scholar, were searched up from inception to April 2021. Clinical trials and animal studies that assessed the effects of GTE on PCOS were eligible for inclusion. RESULTS: Of 314 articles found in the search, four human studies and four animal studies were included. All studies in humans showed the effects of GTE on weight loss. GTE's effect on decreasing testosterone levels in humans and LH levels in animals were also reported. In addition, increases in FSH and progesterone levels in animal models were observed. Although GTE improved fasting blood sugar and insulin levels, the effect of GTE on inflammatory parameters, such as TNF-alpha and IL-6 and antioxidant status, was limited to animal studies. CONCLUSION: Therefore, this review suggests that GTE could be considered a potential agent to attenuate PCOS complications mainly due to its effect on weight loss and glycemic levels. However, more studies are needed to formulate conclusions about the effects and mechanisms of GTE in PCOS.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".