Bibliometrics And Visualisation Analysis Of Literature On Intrauterin Insemination In Obstetrics Gynecology Research Area (2013-2023)
Bibliographic record
Abstract
Objective: Intrauterine insemination (IUI) is a non-invasive, cost-effective assisted reproductive technique widely recognized as the primary intervention and first-line treatment for unexplained infertility. However, no comprehensive bibliometric studies have been conducted to analyze the latest findings and developments in this field. This study aims to evaluate the scientific literature on IUI. Materials and Methods: This bibliometric study utilized the Web of Science database to identify articles related to IUI. All search results were exported and cited in plain text formats to create source files for analysis. The data were then examined using Biblioshiny (version 2.0) and VOSviewer software. The analysis covered annual publication trends, authorship distribution by country and academic institution, journal distribution, author productivity, and keyword usage. Results: As of October 1, 2023, the Web of Science SCIE database contained 24,835 publications, with 18,425 published since 2000 and 9,750 since 2013. After filtering, 964 articles from 55 different sources were analyzed. The dataset, spanning 2013 to 2023, had a mean document age of 5.21 years and an average of 14.95 citations per document, with an annual growth rate of 5.27%. The United States, the Netherlands, and China were the top three contributing countries. Leading academic affiliations included the University of Amsterdam, Harvard University, and McGill University. "Fertility and Sterility" and "Human Reproduction" were the most frequently cited journals. Keyword analysis identified key terms in IUI research, highlighting emerging trends and research priorities. Conclusions: This study provides an overview of the current state and trends in IUI research. The findings offer valuable insights into collaboration patterns, research hotspots, and emerging frontiers, which may benefit researchers and clinicians in the field.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.061 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".