The top 100 most cited articles in helical tomotherapy: a scoping review
Bibliographic record
Abstract
Objective: The purpose of this scoping review was to explore the top 100 most cited articles in helical tomotherapy (HT) through bibliometric analysis and visualization tools, help researchers comprehensively understand the research hotspots of HT, and provide clear and intuitive network visualization. Methods: The Web of Science Core Collection and the search strategy of "Title (TI)=(tomotherapy)" were used to search for articles related to HT as of 27 May 2023. The top 100 most cited articles were obtained by sorting "citations: highest first". From these top 100 most cited articles, the following information was extracted: journals, years and months, countries, authors, types of tumor treated, and topics. The VOSviewer software was introduced for visualizing all the articles related to HT. Results: The top 100 most cited articles in HT were published between 1999 and 2019. The citation counts of these articles ranges from 326 to 45, with a total of 8,422 citations at the time of searching. The index of citations per year (CPY) ranges from 22.32 to 2.45. These articles originated from 17 countries, with most publications from the United States (n=50), followed by Canada (n=12), Italy (n=10), Germany (n=7) and Belgium (n=5). The International Journal of Radiation Oncology, Biology, Physics published the highest number of articles (n=31), followed by Radiotherapy and Oncology (n=20), Medical Physics (n=13) and Strahlentherapie und Onkologie (n=12). In terms of specific tumor types, head and neck cancer (n=15) is the most common disease, followed by cancers with complex target structures (n=14), breast cancer (n=12), prostate cancer (n=10) and lung cancer (n=8). The most common research topics also include dosimetric comparison (n = 44), quality assurance (n = 12) and Megavoltage CT (n = 8). Conclusion: This scoping review provides a comprehensive list of the 100 most cited articles in HT. This analysis offers valuable insights into the current research directions of HT that can be utilized by researchers, clinicians, and policy-makers.
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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 | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.071 | 0.057 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".