Trends and bibliometric analysis on pediatric anesthesia from 2002 to 2022: A review
Bibliographic record
Abstract
Pediatric anesthesia is one of the most concerning topics in our society. However, there is still a lack of a comprehensive overview of the research base and of future trends. This study aimed to guide beginners quickly learn the academic research on pediatric anesthesia and do their own studies by analyzing the articles of this field in the latest 21 years through bibliometric analysis. Literature scanning was conducted with the Web of Science database. Microsoft Excel, SPSS, VOSviewer, and CiteSpace were in this review. There was an increasing trend of articles on pediatric anesthesia, based on the analysis of 11,591 included articles. The top 3 most productive countries were the United States of America (4538), Canada (730) and Turkey (688). The most productive institutions were Boston Childrens hospital, Childrens Hospital Philadelphia and Ohio State University. Tobias, Joseph D (141), Kim, Hee-Soo (40) and Curley, Martha A Q (38) were the most active authors. Habre W (2017), Gross JB (2002) and Cravero JP (2009) are the articles cited more than 100 times during the analysis years. Anesthesia and Analgesia, Anesthesiology, Pediatric Anesthesia, were the core journals in this field. Cohort, simulation, sleep, postoperative complication are strongest burst keywords in recent years. This article summarizes the authoritative institutions, authors, literatures and frontier hotspots on pediatric anesthesia. Itwill be a valuable literature review and help beginners to quickly get started in the field, reduce unnecessary clueless and aimless learning, and greatly improve learning efficiency.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.166 | 0.494 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".