Advancements in Telemedicine for Surgical Practices: A Comprehensive Bibliometric Analysis
Bibliographic record
Abstract
Purposes: This study aims to use bibliometric analysis to explore the development, research hotspots, and trends in the field of telemedicine for surgical practices (TSPs). Methods: A bibliometric analysis of 3,235 documents from the Web of Science Core Collection was conducted, spanning from 2004 to 2022. Citespace (6.2.R5) was used to perform a bibliometric analysis. Results: The findings highlight a marked escalation in researches of TSPs, particularly between 2019 and 2022, aligning with the COVID-19 pandemic. The Telemedicine and e-Health Journal was the most productive journal with 118 publications, and Journal of Telemedicine and Telecare had the most citations ( n = 700). Howard S. An and Mohammad El-sharkawi had the most papers ( n = 8). Harvard University was the most prolific institution ( n = 103). The United States, England, and Canada were identified as the predominant contributing countries with a total of 1,521 publications. There was a notable shift in research focus areas over time, with recent emphasis being placed on pediatric surgery, COVID-19-related studies, and orthopedics. Future trends may involve teleconsulting, ameliorating the quality and safety of telemedicine, and improving satisfaction levels of patients and caregivers when they are using telemedicine. Conclusions: The study reveals that the rapid and sustained advancement in TSPs, significantly driven by the COVID-19 pandemic, and huge gaps between developed countries and developing countries. This study also reflects the current hotspots and future directions for TSPs.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.157 | 0.216 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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, 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".