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Record W2955334283 · doi:10.4102/jir.v4i1.50

The contribution of hyperinsulinemia to the hyperandrogenism of polycystic ovary syndrome

2019· article· en· W2955334283 on OpenAlexaff
Nadia B. Pateguana, A. N. Janes

Bibliographic record

VenueJournal of Metabolic Health · 2019
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHyperinsulinemiaPolycystic ovaryHyperandrogenismTestosterone (patch)MedicineEndocrinologyInternal medicineSex hormone-binding globulinHormoneInsulinInsulin resistanceAndrogen

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.281
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2019
Admission routes1
Has abstractyes

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