The top 100 most cited articles on fertility-sparing treatments for cervical cancer: A bibliometric analysis
Bibliographic record
Abstract
Background The primary objective of this paper was to assess and analyze the top 100 most cited articles currently cited in studies of fertility-sparing treatments for cervical cancer. Methods Searching the Web of Science Core Collection database for the top 100 most cited articles on fertility-sparing treatments for cervical cancer, different aspects of the articles were analyzed, including countries, journals, institutions, authors, keywords and topics. Results The search was conducted up to August 2023, and the number of citations for the top 100 articles ranged from 19 to 212. These articles originated from 28 different countries, with Professor Plante, M. from Canada and Professor Sonoda, Y. from the USA having the highest number of articles, both with 10. Professor Plante, M. was the first author of 9 articles and corresponding author of 9 articles. The Memorial Sloan Kettering Cancer Center in the USA published the most articles (21) and received a total of 258 citations. Gynecologic Oncology published 37 of the top 100 articles, with 524 citations and an average of 14.16 citations per article. Conclusions The study concludes that the USA has made the most significant contributions to this field based on the number of articles, authors, and institutions. Additionally, keyword clustering and burst analysis revealed the research hotspots and future trends in this area.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.131 | 0.127 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".