Research trends on chemotherapy induced nausea and vomiting: a bibliometric analysis
Bibliographic record
Abstract
Background: CINV is a frequent adverse response to cancer treatment. There is still much to learn about the pathophysiology and initiating event of CINV, which necessitates continued research despite decades of effort. Identifying the current foci of the complex disease and assessing the scientific impact of pertinent study are made more difficult by the abundance of publications on CINV. Therefore, our goals in this article are to evaluate developments in this field, examine patterns in research domains, and gauge the expansion of CINV research production globally. Methods: Articles about CINV published between 2012 and 2022 were found by searching the Web of Science Core Collection of Clarivate Analytics. The number of publications over time was visualized using Microsoft Office Excel 2019. CiteSpace and VOSviewer were utilized to create knowledge maps that analyzed collaborations between nations, organizations, and writers. They also presented the history of CINV research and highlighted its current areas of focus. Results: In this study, 846 papers in all were assessed. Most publications (237, 28.01%) came from the United States. University of Toronto was the most productive institution (34, 4.01%). With 25 articles published, or 2.96% of the total, Aapro Matti published the most. The most frequently published journal was found to be Supportive Care (158, 18.68%). "Palonosetron," "Moderately emetogenic chemotherapy," "5-HT3 receptor antagonist," and "Neurokinin 1 receptor antagonists" were considered the hot topics. It can be seen that the research focus is on the drug treatment of chemotherapy-induced nausea and vomiting. Conclusion: Through bibliometric analysis, we were able to gain profound insights into CINV research for the first time. Researchers looking to uncover research frontiers and comprehend important information in this discipline may find the study's findings useful.
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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.163 | 0.220 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".