Relationship of Premenstrual Dysphoric Disorder With Bipolar Disorder
Bibliographic record
Abstract
Since depression represents the most predominant mood polarity in bipolar disorder (BD), the prevalence rates of a diagnosis of premenstrual dysphoric disorder (PMDD) in women with BD and those of a diagnosis of BD in women with PMDD deserve systematic review. A systematic search of PubMed, EMBASE, CINAHL, PsycINFO, and Cochrane Reviews databases was carried out on November 19, 2021, using the terms [late luteal phase disorder OR premenstrual dysphoric disorder] AND comorbidity AND bipolar disorder. Articles from 1987-2021 were searched. Case studies, intervention studies, reviews, and systematic analyses were excluded. All studies that included a diagnosis of PMDD and BD were included. The selected articles were reviewed to extract data using a data extraction form developed for this study. A total of 5 studies were included in the review. Extant literature, although limited, suggests that PMDD is more common among women with BD than in the general population. Similarly, BD is more common among women with PMDD than in the general population. The proportion of people with PMDD and diagnosed with BD ranged from 10% to 15%. Conversely, the proportion of people with BD who received a diagnosis of PMDD ranged from 27% to 76%. Only a small number of relevant studies were available, and the findings from these were limited by the failure to employ prospective monitoring of symptoms-perhaps the most important feature necessary for confirming PMDD and differentiating it from premenstrual exacerbation of BD. Given the important clinical and heuristic implications, prospective studies are needed to clarify the relationship between the two disorders in order to improve their detection, diagnosis, and treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".