The 100 Most Cited Papers in Radiotherapy or Chemoradiotherapy for Cervical Cancer: 1990–2020
Bibliographic record
Abstract
OBJECTIVE: This work aims to analyze the 100 most cited papers in radiotherapy or chemoradiotherapy for cervical cancer. METHODS: The 100 most cited papers in radiotherapy or chemoradiotherapy between 1990 and 2020 were identified with Thompson Reuters Web of Science citation indexing on August 24, 2020. The publication years, source titles, countries/regions, total citations, and average citations per year were extracted from the Web of Science. The research type and research domain were classified by the authors. RESULTS: These 100 papers were cited a total of 28,714 times, and the median number of citations was 169.5 (ranging from 116 to 1,700 times). The most cited paper was "Concurrent cisplatin-based radiotherapy and chemotherapy for locally advanced cervical cancer" by Rose et al., with a total citation of 1,700 times. The International Journal of Radiation Oncology Biology Physics (40 papers), Radiotherapy and Oncology (14 papers), and the Journal of Clinical Oncology (12 papers) published the largest number of papers. USA (47 papers), Austria (18 papers), Canada (13 papers), and England (13 papers) contributed the largest number of papers. Image-guided adaptive brachytherapy (IGABT) had the largest number of papers (25 papers). Concurrent chemotherapy was the most cited research domain, with 10,663 total citations and 592.4 citations per paper. CONCLUSION: In the present study, we conducted a bibliometric analysis of the 100 most cited papers in radiotherapy or concurrent chemoradiotherapy for cervical cancer in the past 30 years. IGABT and concurrent chemotherapy were the most cited research domains.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.067 | 0.073 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".