Hotspots and Frontiers in Arrhythmias During Pregnancy: A Bibliometric Analysis
Bibliographic record
Abstract
Background: Arrhythmias in pregnancy have become an increasingly significant concern for maternal and fetal well-being, reflecting a rising prevalence trend. This bibliometric analysis sought to delineate global research trajectories, pinpoint principal contributors, and underscore nascent areas of interest in this domain. Methods: We retrieved publications concerning arrhythmias in pregnant women from 1996 to 2025 from the Web of Science Core Collection. A bibliometric analysis was performed utilizing VOSviewer, CiteSpace, and the R package “bibliometrix” to delineate co-authorship networks, institutional collaborations, and patterns of keyword co-occurrence. Results: In total, 1042 publications were identified with an annual growth rate of 4.9%. The USA led in total publications (300, 28.8%). Productive institutions featured the University of Toronto (95) and Harvard University (94). The American Journal of Cardiology contributed the highest number of articles (25). Roos-Hesselink JW was identified as a foremost researcher, with 23 publications and an H-index of 19. Keyword analysis revealed “management” as a central theme, while “outcome”, “long QT syndrome”, and “cardiovascular disease” were emerging themes. Conclusion: This bibliometric study presents a thorough overview of international research on arrhythmias in pregnant women. It identifies key contributors, influential institutions, and developing research topics, offering potential insights for optimizing pregnancy management, enhancing clinical outcomes, and progressing the treatment of cardiovascular and heart-related conditions during gestation. Keywords: arrhythmia, pregnancy, bibliometric analysis, VOSviewer, CiteSpace
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.176 | 0.227 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".