A global perspective: Trends and insights in premenstrual disorder comorbidity research by bibliometric analysis (1999–2023)
Bibliographic record
Abstract
BACKGROUND: Premenstrual disorders represent a constellation of incapacitating gynecological disorders with numerous coexisting conditions. The identification and comprehension of disease-related comorbidities are of paramount importance in the medical field. This study aims to systematically review existing research and identify potential focus areas through bibliometric analysis. METHODS: One hundred forty-nine publications on comorbidity in psychotic mood disorders between 1999 and 2023 were retrieved from the Web of Science Core Collection. The data was analyzed using bibliometric tools such as CiteSpace 6.1.R2 and VOSviewer. Specifically, the study examined the annual publication count, contributions by country and institution and details on journals, authors, citation counts, and keywords. RESULTS: The data were retrieved on August 3, 2023. The annual number of publications showed an upward trend from 1999 to 2023. Globally, the US and Canada presented the highest publication counts and served as core research regions. Meanwhile, McMaster University and Harward University exhibited the highest output and influence by institution. In terms of author, Frey BN (McMaster University, Canada) was the most prolific with leading academic influence, while Lieb R (University of Basel, Switzerland) and Wittchen HU (Technical University Dresden, Germany) were the most cited authors. The American Journal of Psychiatry (impact factor = 17.7, 2023) was the most frequently cited journal. Furthermore, significant overlapping between premenstrual syndrome and premenstrual dysphoric disorder warrants further investigation, and the intrinsic connection between premenstrual dysphoric disorder and bipolar disorder is a rising focus. Temporally, research shifted from prevalence surveys to diagnostic and mechanistic studies. CONCLUSIONS: This bibliometric study comprehensively analyzes the current state of research on physical and mental health comorbidities. North America became a prominent leader in contributions from countries, institutions, authors, and journals. Additionally, the study underscores the potential for further exploration of comorbidity between physiological and psychiatric conditions, suggesting a promising avenue for future research efforts.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.132 | 0.222 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".