The contribution of hyperinsulinemia to the hyperandrogenism of polycystic ovary syndrome
Bibliographic record
Abstract
Background: Polycystic Ovary Syndrome (PCOS) is a very common endocrine disorder of young women.Aim: The proper treatment of PCOS requires a thorough understanding of the underlying cause of disease. In this article, we review the extent to which hyperinsulinemia contributes to the development of PCOS.Setting: The goal of this review was to assess the current literature on the contribution of hyperinsulinemia to the hyperandrogenism of polycystic ovary syndrome in hopes of promoting future research and advancements in clinical treatments for women with PCOS focusing on this major contributing factor, hyperinsulinemia.Method: A review of published peer-reviewed literature was conducted by searching the keywords.Results: Excessive insulin causes both the overproduction of testosterone and decreased sex hormone binding globulin (SHBG) levels seen in PCOS, both of which collaborate in creating an increased testosterone effect.Conclusion: The majority of research and evidence shows that the hyperandrogenism of PCOS is likely caused by hyperinsulinemia. Yet the conventional treatment of hyperandrogenic symptoms in women with PCOS is not directed towards correcting this underlying hyperinsulinemia. Further research is needed to assess how the treatment of the hyperinsulinemia through lifestyle would compare to the current treatment of hyperandrogenemia through testosterone-lowering drugs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".