Bibliometric and visual analysis of chemotherapy-induced nausea and vomiting (2004-2023)
Bibliographic record
Abstract
Background: Patients undergoing chemotherapy often encounter troubling and common side effects, notably Chemotherapy-induced nausea and vomiting (CINV). This side effect not only impairs the patient's quality of life but could also result in the interruption or discontinuation of the chemotherapy treatment. Consequently, research into CINV has consistently remained a focal point in the realm of clinical medicine. In this research domain, bibliometric analysis has not been conducted. The purpose of this study is to deliver a thorough summary of the knowledge framework and key areas of interest in the field of Chemotherapy-induced nausea and vomiting, using bibliometric methods. This approach aims to furnish novel concepts and pathways for investigators working in this area. Methods: Publications focusing on Chemotherapy-induced nausea and vomiting, spanning from 2004 to 2023, were identified using the Web of Science Core Collection (WoSCC) database. Tools such as VOSviewer, CiteSpace, and the R package "bibliometrix" were employed for this bibliometric analysis. Results: This research covers 734 publications from 61 countries, with the United States and China being the primary contributors. There has been a significant rise in the volume of papers published in the most recent decade compared to the one before it, spanning over the past twenty years. However, the annual publication rate in the last ten years has not shown a significant upward trend. The University of Toronto, Merck & Co., Sun Yat-sen University, and Helsinn Healthcare SA emerged as the principal research institutions in this field. Supportive Care in Cancer stands out as the most frequently published and cited journal in this domain. These works are contributed by 3,917 authors, with Rudolph M Navari, Matti Aapro, Shimokawa Mototsugu, and Lee Schwartzberg being among those who have published the most. Paul J. Hesketh is notably the most co-cited author. The primary focus of this research field lies in exploring the mechanisms of CINV and the therapeutic strategies for managing it. Key emerging research hotspots are represented by terms such as "Chemotherapy-induced nausea and vomiting," "nausea," "vomiting," "chemotherapy," and "antiemetics." Conclusion: This represents the inaugural bibliometric study to thoroughly outline the research trends and advancements in the field of CINV. It highlights the latest research frontiers and trending directions, offering valuable insights for scholars engaged in studying CINV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.153 | 0.205 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".