Comparing rosiglitazone with ethinylestradiol/cyproterone acetate in the treatment of polycystic ovary syndrome
Bibliographic record
Abstract
Recently, an important role of insulin resistance has been demonstrated in polycystic ovary syndrome. New treatment strategies have emerged from this association, as well as a will to prevent long-term metabolic complications of the syndrome, namely metabolic syndrome, Type 2 diabetes mellitus and cardiovascular disease. Rosiglitazone, a member of the thiazolidinedione family, appears to efficiently treat oligoanovulation, menstrual irregularity and hirsutism but it might not be the best treatment for acne. By comparison, ethinylestradiol/cyproterone acetate is not only better than rosiglitazone in controlling menstrual irregularity and acne but also appears to be equally effective in alleviating hirsutism, although it is inferior in restoring fertility. As for long-term complications, rosiglitazone appears to treat the metabolic syndrome and prevent insulin resistance, Type 2 diabetes and cardiovascular disease, while no similar benefit is expected with ethinlyestradiol/cyproterone acetate. Both treatments are equally effective in preventing endometrial neoplasia. In summary, both rosiglitazone and ethinlyestradiol/cyproterone acetate are efficient in treating polycystic ovary syndrome, but there might be a significant advantage of rosiglitazone in preventing long-term metabolic complications of the syndrome.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".