Global research trends on the links between prostate cancer and erectile dysfunction between 2003 and 2023: A bibliometrics and visualized study
Bibliographic record
Abstract
Background: The incidence of prostate cancer (PC) has increased in recent years. Erectile dysfunction (ED) after prostate cancer treatment has aroused extensive attention. Bibliometric analysis was designed to investigate a systematic understanding of developments between PC and ED during the past 20 years. Methods: Literatures on PC and ED were retrieved from the Web of Science Core Collection database (WoSCC). By using the VOS viewer and CiteSpace software to analysis the metrics of bibliometric literature, such as number of articles, journals, countries, institutions, authors, keywords and associated information. The number of publications per year was statistically analysed and plotted thorough Microsoft Office. In addition, Pajek software was used to adjust the visual map. Results: A total of 2332 screened articles were included in the analysis. The Journal of Sexual Medicine, ranking first among the analysed journals, published 235 articles. The United States and Canada were leaders in PC and ED research. There is a need to strengthen inter-agency cooperation in this area of research on a global scale. Mulhall JP, as the most prolific author in this area of research, published 80 articles. And Rosen RC was the author with the most co-citated (693 co-citated). The main research focus on the prevention, treatment and management of ED after PC treatment in this field through the keyword analysis. Conclusions: Research on PC and ED is expected to expand further worldwide. We found ED, as new sustainable treatment modalities, scientific postoperative management and psychological interventions for patients, may become the research hotspots and should be closely concerned in this study.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.111 | 0.170 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".