Bibliographic record
Abstract
Polycystic ovarian syndrome is a common disorder associated with a significant long-term risk of developing type 2 diabetes and cardiovascular diseases. Insulin resistance and hyperinsulinemia play an important role in its pathophysiology and therefore insulin sensitizers have been proposed as a possible treatment option for this condition. In this review, pertinent literature is described that supports the use of insulin sensitizers for the management of short-term (fertility and hyperandrogenism) as well as long-term (type 2 diabetes, cardiovascular diseases and endometrial cancer) clinical issues of the syndrome. There is sufficient evidence in the literature to support the initial use of insulin sensitizers for fertility and the chronic treatment of hyperandrogenism. Furthermore, insulin sensitizers may prevent type 2 diabetes or cardiovascular diseases, whereas some evidence suggests that oral contraceptives could increase these risks. Therefore, although oral contraceptives may provide a more reliable control of menstrual disorders, insulin sensitizers should be considered as a preferential treatment option in women with polycystic ovarian syndrome at an increased risk of developing type 2 diabetes or cardiovascular disease, especially if they do not need contraception.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".