Global trends in anesthetic research over the past decade: a bibliometric analysis
Bibliographic record
Abstract
Background: Anesthesia is the reversible inhibition of function of the central and/or peripheral nervous system using drugs or other means to ensure a successful operation. This inhibition is mainly manifested as a loss of sensation, especially pain. Methods: Bibliometric analysis was used to identify the characteristics, hotspots, and frontiers of global anesthesiology scientific output during the past 10 years. Literatures between 2011 and 2020 in the Web of Science Core Collection (WoSCC) were reviewed and analyzed. VOSviewer was used to visualize trends and hotspots in anesthesia research. Results: A total of 16,213 publications were retrieved and results showed that there was no significant correlation between the number of articles published each year and the year of publication. England had the most published papers, the greatest number of citations (NC), and the highest h-index. The University of London and the British Journal of Anesthesia were the richest affiliate and journal, respectively. The publication written by Heidenreich et al. had the highest global citation score (GCS). Conclusions: Our research found that global publications on anesthesia have raised. Recently, “surgery”, “management”, “propofol”, and “analgesia” appeared most frequently, which were active areas of research. In the future research, pain management, pediatric anesthesia, safety, dexmedetomidine, et al. will be the hotspot and mainstream trend of research.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.153 | 0.205 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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, 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".