Top 100 most-cited articles on renal cell carcinoma: A bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: To analyze the top 100 most-cited articles on renal cell carcinoma (RCC) using bibliometric methods based on the Web of Science core collection database and to explore the research status, hotspots, and emerging trends in RCC. METHODS: The literature on RCC was searched in the Web of Science core collection database using a specific search strategy, and the types of literature were limited to articles and reviews, with no restrictions to language and publication date. The top 100 articles with the highest number of citations were extracted after the manual screening. The publication year, the number of citations, authors, country, institution, journal, and keywords of these articles were collected and analyzed. Descriptive statistics and visual analysis were performed using Microsoft Excel, VOSviewer, CiteSpace, R, and SPSS. RESULTS: The number of citations of the top 100 articles varied from 541 to 4530, with a median citation count of 807.5, and the citation rates ranged from 13.8 to 448.4 citations per year. Motzer RJ (n = 22), Escudier B (n = 13), Rini BI (n = 13), and Hutson TE (n = 11) were major contributors to this research area, with Motzer RJ publishing 16 articles as the first author. The US (n = 73), France (n = 5), Canada (n = 4), and Sweden (n = 4) were the leading countries for RCC studies. MEMORIAL SLOAN KETTERING CANCER CENTER (n = 22) was the institution with the highest number of publications. These 100 articles were derived from 24 journals, and the New England Journal of Medicine had the largest number of articles published (n = 18, impact factor = 91.245). The keyword co-occurrence network analysis showed that research hotspots in this field included molecular mechanisms of RCC development and progression, surgical treatment, targeted drug-related clinical trials, and immunotherapy. CONCLUSION: We analyzed the top 100 articles with the highest number of citations in the field of RCC and identified the influential authors, countries, institutions, and journals in this field. This study also presented the current research status, hotspots, and future trends in RCC.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.151 | 0.142 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".