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Polycystic Ovary Syndrome and Metabolic Syndrome

2009· book-chapter· en· W939387880 on OpenAlexaff
Marie-Hélène Pesant, Jean‐Patrice Baillargeon

Bibliographic record

VenueHumana Press eBooks · 2009
Typebook-chapter
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPolycystic ovaryMetabolic syndromeInsulin resistanceEtiologyMedicineMetabolic disorderEndocrinologyInternal medicineDiabetes mellitusBioinformaticsBiology

Abstract

fetched live from OpenAlex

The polycystic ovary syndrome (PCOS) is a very common disorder with important short-term and long-term consequences. Indeed, affected women manifest many clinical and biochemical features of metabolic syndrome, putting them at increased risk for diabetes and cardiovascular diseases. In the past 15 years, the key role of insulin resistance (IR) in the pathogenesis of PCOS has been stressed, and this common etiology between the two syndromes might account for their similarities. In fact, PCOS is considered a consequence of IR in the same way as metabolic syndrome, and women at risk for PCOS are also subject to the development of metabolic syndrome. Therefore, this chapter will explore the relationships between the metabolic syndrome and 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.004

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 head0.040
GPT teacher head0.252
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2009
Admission routes1
Has abstractyes

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