Complementary administration of curcumin in PCOS: a systematic review
Bibliographic record
Abstract
INTRODUCTION: The clinical benefit of curcumin, the active ingredient in turmeric, among women diagnosed with polycystic ovarian syndrome (PCOS) is unclear. In this systematic review, we aim to provide a brief review of the existing literature on the association between curcumin supplementation and PCOS. EVIDENCE ACQUISITION: Published articles relevant to the topic was obtained through extensive search using relevant keywords such as "polycystic ovarian syndrome," "PCOS," "turmeric," and "curcumin." Inclusion criteria included studies that investigated PCOS and turmeric/curcumin, while studies that investigated the use of curcumin in other gynecological disorders, infertility, rodent studies, and non-RCTs were excluded. EVIDENCE SYNTHESIS: A total of 14 articles were found and only five studies were incorporated based on the exclusion criteria. The main findings were that curcumin supplementation aids in improvement of lipid and glycemic profiles, Body Mass Index (BMI) and lowers androgen levels associated with PCOS. CONCLUSIONS: We shed light on additional therapeutic management for PCOS other than the conventional treatment. Further studies are required with larger sample sizes and diverse patient population to derive definitive conclusions regarding benefits of curcumin supplementation in PCOS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".