Menstrual Abnormalities and Polycystic Ovary Syndrome in Women Taking Valproate for Bipolar Mood Disorder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it