Metabolic Basis of Polycystic Ovarian Syndrome; Indications for Biochemical Screening
Bibliographic record
Abstract
BACKGROUND: Polycystic ovary syndrome (PCOS) is a heterogeneous condition with wide range of phenotype the cause of this disorder is unknown, however, increased ovarian androgen production including increased theca cell responsiveness to gonadotropin stimulation, increased pituitary secretion of luteinizing hormone, and hyperinsulinemia have been suggested. Some known risk factors are ethnicity and environmental factors including lifestyle and bodyweight. METHOD: Relevant English language studies from January 1995 to September 2015 were identified, reviewed, synthesized and discussed extensively. RESULT: various forms of PCOS, and the underlying mechanisms; and highlights biochemical, morphological and metabolic hallmarks of PCOS, including trends and emerging phenotypes; and presents a simplified synthesis that integrates current understanding of biochemical and metabolic endocrinology of PCOS are summarized. As no generally accepted criteria exist for its diagnosis, some existing diagnostic criteria that cut across different geographical regions are also highlighted. CONCLUSION: Indeed, emerging evidence points that the severity of menstrual problems could serve as a predictor of the like-hood of insulin resistance in women of reproductive age, suggesting the need for routine metabolic screening and early intervention in PCOS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".