Menstrual Abnormalities and Polycystic Ovary Syndrome in Women Taking Valproate for Bipolar Mood Disorder
Bibliographic record
Abstract
BACKGROUND: Valproate treatment has been associated with high rates of menstrual abnormalities, hyperandrogenism, and polycystic ovaries in women with epilepsy. This pilot study investigated whether valproate treatment had the same associations in women with bipolar disorder. METHOD: One hundred forty outpatient women with a DSM-IV diagnosis of bipolar disorder (aged 15-45 years) were surveyed on their medical, psychiatric, and reproductive health history. Thirty-two women met entry criteria for the study and were divided into 2 groups: (1) those currently receiving valproate (valproate, N = 17) and (2) those who were not currently taking valproate (nonvalproate, N = 15). These 2 groups were compared with a normal (never diagnosed with a psychiatric disorder) control group of 22 women. Women in the valproate group with current menstrual problems (N = 7) underwent further assessment for the presence of polycystic ovaries and hyperandrogenism. RESULTS: The age at onset of menses, mean length of menstrual cycle, and mean length of menses were not significantly different between the groups. Significantly more women reported menstrual abnormalities in the valproate group (47%) than women not receiving valproate (13%) and controls (0%). Forty-one percent of women with bipolar disorder taking valproate had polycystic ovary syndrome. CONCLUSION: These results suggest high rates of menstrual disturbances and polycystic ovary syndrome in women with bipolar disorder currently receiving valproate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".