Increased illness burden in women with comorbid bipolar and premenstrual dysphoric disorder: data from 1 099 women from STEP‐BD study
Bibliographic record
Abstract
BACKGROUND: The impact of comorbid premenstrual dysphoric disorder (PMDD) in women with bipolar disorder (BD) is largely unknown. AIMS: We compared illness characteristics and female-specific mental health problems between women with BD with and without PMDD. MATERIALS & METHODS: A total of 1 099 women with BD who participated in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) were studied. Psychiatric diagnoses and illness characteristics were assessed using the Mini International Neuropsychiatric Interview. Female-specific mental health was assessed using a self-report questionnaire developed for STEP-BD. PMDD diagnosis was based on DSM-5 criteria. RESULTS: Women with comorbid BD and PMDD had an earlier onset of bipolar illness (P < 0.001) and higher rates of rapid cycling (P = 0.039), and increased number of past-year hypo/manic (P = 0.003), and lifetime/past-year depressive episodes (P < 0.05). Comorbid PMDD was also associated with higher proportion of panic disorder, post-traumatic stress disorder, generalized anxiety disorder, bulimia nervosa, substance abuse, and adult attention deficit disorder (all P < 0.05). There was a closer gap between BD onset and age of menarche in women with comorbid PMDD (P = 0.003). Women with comorbid PMDD reported more severe mood symptoms during the perinatal period and while taking oral contraceptives (P < 0.001). DISCUSSION: The results from this study is consistent with research suggesting that sensitivity to endogenous hormones may impact the onset and the clinical course of BD. CONCLUSIONS: The comorbidity between PMDD and BD is associated with worse clinical outcomes and increased illness burden.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".