Comorbid Premenstrual Dysphoric Disorder and Bipolar Disorder: A Review
Bibliographic record
Abstract
Bipolar disorder (BD) differs in its clinical presentation in females compared to males. A number of clinical characteristics have been associated with BD in females: more rapid cycling and mixed features; higher number of depressive episodes; and a higher prevalence of BD type II. There is a strong link between BD and risk for postpartum mood episodes, and a substantial percentage of females with BD experience premenstrual mood worsening of varying degrees of severity. Females with premenstrual dysphoric disorder (PMDD)-the most severe form of premenstrual disturbances-comorbid with BD appear to have a more complex course of illness, including increased psychiatric comorbidities, earlier onset of BD, and greater number of mood episodes. Importantly, there may be a link between puberty and the onset of BD in females with comorbid PMDD and BD, marked by a shortened gap between the onset of BD and menarche. In terms of neurobiology, comorbid BD and PMDD may have unique structural and functional neural correlates. Treatment of BD comorbid with PMDD poses challenges, as the first line treatment of PMDD in the general population is selective serotonin reuptake inhibitors, which produce risk of treatment-emergent manic symptoms. Here, we review current literature concerning the clinical presentation, illness burden, and unique neurobiology of BD comorbid with PMDD. We additionally discuss obstacles faced in symptom tracking, and management of these comorbid disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".