International Bibliometric Analysis of Literature on Intrauterine Insemination
Bibliographic record
Abstract
Intrauterine insemination (IUI) is an artificial insemination method that employs specialized devices to introduce spermatozoa into the uterus. IUI is utilized to address challenges associated with poor reproductive outcomes and to optimize the productivity of livestock. Consequently, its application and utilization has gained global attention and is being continuously developed. Therefore, this study aimed to analyze the global literature on intrauterine insemination research over the preceding decade. Documents related to intrauterine insemination research were presented in the results of a bibliometric study indexed in the Scopus database for the period 2012–2022. On a global scale, the total number of identified documents amounted to 2,721, with an average annual production of 272 documents. Ten countries worldwide were identified as leading contributors to research publications on intrauterine insemination, including the United States with the most document production (n=643), followed by Turkey (n=175), India (n=173), China (n=161), United Kingdom (n=159), Iran (n=153), Netherlands (n=152), Canada (n=140), France (n=131), and Italy (n=129). This study of novelty comprehensive bibliometric analysis to map a decade of global research trends in intrauterine insemination across both human and animal applications. Unlike previous narrative reviews that focused on clinical or technical aspects, this research highlights global collaboration networks, publication dynamics, and emerging thematic hotspots within the IUI domain. Publications related to intrauterine insemination showed an upward trajectory from 2012 until 2021, followed by a decline in 2022. The findings from this analysis provide valuable guidance for future research in the field of intrauterine insemination.
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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) | 0.003 | 0.002 |
| Bibliometrics | 0.241 | 0.304 |
| 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) | 0.007 | 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".