Global trends and prospects in health economics of robotic surgery: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Over 10 million robotic surgeries have been performed. However, the cost and benefit of robotic surgery need to be evaluated to help hospitals, surgeons, patients, and payers make proper choices, making a health economic analysis necessary. The authors revealed the bibliometric profile in the field of health economics of robotic surgery to prompt research development and guide future studies. MATERIALS AND METHODS: The Web of Science Core Collection scientific database was searched for documents indexed from 2003 to 31 December 2022. Document types, years, authors, countries, institutions, journal sources, references, and keywords were analyzed and visualized using the Bibliometrix package, WPS Office software, Microsoft PowerPoint 2019, VOSviewer software (version 1.6.18), ggplot2, and Scimago Graphica. RESULTS: The development of the health economics of robotic surgery can be divided into three phases: slow-growing (2003-2009), developing (2010-2018), and fast-developing (2019-2022). J.C.H. and S.L.C. were the most active and influential authors, respectively. The USA produced the most documents, followed by China, and Italy. Korea had the highest number of citations per document. Surgical Endoscopy and Other Interventional Techniques accepted most documents, whereas Annals of Surgery, European Urology, and Journal of Minimally Invasive Gynecology had the highest number of citations per document. The Journal of Robotic Surgery is promising. The most-cited document in this field is New Technology and Health Care Costs - The Case of Robot-Assisted Surgery in 2010. The proportion of documents on urology is decreasing, while documents in the field of arthrology are emerging and flourishing. CONCLUSION: Research on the health economics of robotic surgery has been unbalanced. Areas awaiting exploration have been identified. Collaboration between scholars and coverage with provisions for evidence development by the government is needed to learn more comprehensively about the health economics of robotic surgery.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.106 | 0.207 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".