Varicocele‐Induced Male Infertility: A Bibliometric Perspectives of Mechanistic Insights and Clinical Trends
Bibliographic record
Abstract
Background Male infertility (MI) has become a global public health challenge, and varicocele (VC) is considered a common cause of male reproductive disorders. Although scientific research on MI has increased in recent years, there is still a significant lack of systematic integration and analysis of related publications. Objective This study employs bibliometric methods to conduct a comprehensive visual analysis of research on MI and VC from 2004 to 2024, aiming to depict the evolutionary trajectory, knowledge framework, and research frontiers in this field. Methods Tools such as VOSviewer, CiteSpace, Scimago Graphica, Pajek, R‐bibliometrix, and R packages were used for bibliometric analysis and visualization of literature retrieved from the Web of Science database. Results From 2004 to 2024, publications on MI and VC have shown exponential growth, with the most pronounced increase occurring in 2020. At the national level, United States and China were the major contributors. Institutionally, the Cleveland Clinic and McGill University emerged as leading research centers. At the author level, Agarwal A. and Esteves S.C. were identified as the most influential contributors. Regarding journals, Andrologia and Fertility and Sterility served as major publication platforms. Keyword analysis delineated the key research domains within MI and VC. High‐frequency keywords such as oxidative stress, DNA fragmentation, and spermatogenesis underscored the central research focus on oxidative damage and spermatogenic mechanisms, whereas terms such as varicocelectomy and microsurgery highlighted the sustained clinical interest in surgical interventions. Burst detection suggested a recent shift of research hotspots toward inflammation, lipid peroxidation, obesity, and antioxidant therapies such as vitamin E. Strategic diagram analysis further demonstrated that this field integrates both fundamental and interdisciplinary characteristics. Core topics such as testis and apoptosis were identified, along with well‐developed but relatively isolated directions, collectively reflecting a trend of progressive integration from mechanistic exploration to clinical application. Conclusion Through bibliometric analysis, this study reveals a significant upward trend in research on MI and VC, reflecting the growing academic attention to multiple directions within the field. Moreover, it systematically maps the intellectual structure and developmental trajectory of the discipline, providing a comprehensive framework for understanding its current status, research foci, and emerging trends.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.129 | 0.185 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".