Early Menopause Is a Risk Factor for Postmenopausal Depression in Healthy Women, But Are Depressive Episodes a Risk Factor for Early Menopause?
Bibliographic record
Abstract
Background: This study investigated whether age of menopause in bipolar women is different from that in healthy women and if there is a relationship between age at menopause and previous depressive episodes. Methods: We consecutively evaluated 86 euthymic and postmenopausal women who were older than 33 years and were diagnosed with bipolar disorder according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). The healthy control group comprised 100 individuals of similar age. After the diagnostic interview, bipolar patients completed the Mood Disorders Diagnosis and Follow-Up Form (SKIP-TURK). Results: Age at menopause in bipolar women was earlier than that of the controls (P = 0.001). Age at menopause correlated with age of onset of bipolar disorder (r = 0.453). A strong inverse correlation was found between the age of menopause and total duration of depressive episodes (r = -0.542). Conclusion: Early-onset bipolar disorder is associated with early ages of menopause. Age at menopause seems to be related to duration of depressive period. Considering the effects of hypoestrogenemia on ischemic heart diseases and cognitive impairment, it is important that the risk which is already high for both situations should be reduced, and depressive periods should be prevented. J Clin Gynecol Obstet. 2018;7(3-4):69-71 doi: https://doi.org/10.14740/jcgo504w
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".