A bibliometric analysis of research on pediatric preoperative anxiety (2007–2022)
Bibliographic record
Abstract
Objective: This study aimed to analyze the current state of research on preoperative anxiety in children through CiteSpace, VOSviewer, and the identification of hot spots and frontiers. Method: Relevant data were retrieved from the Web of Science Core Collection using the search terms children and preoperative anxiety. Data were analyzed using VOSviewer (version 1.6.18), CiteSpace (5.7. R5) software, and Scimago Graphica. Results: A total of 622 articles were published between 2007 and 2022, with an increasing trend over time. Kain, Zeev N. (13; 2.09%) and Dalhousie University (15; 2.41%) were the most influential authors and most prolific institutions, respectively. The United States (121; 19.45%) was the country with the most publications. Pediatric anesthesia (55; 8.84%) had the most publications. High-frequency keywords were categorized into three themes, including nonpharmacologic interventions for preoperative anxiety in children, preoperative medications, and risk factors for anxiety; of these, "predictor" (38; 2016) and "sedative premedication" (20; 2016) were the most studied keywords over the past 6 years. "Distraction" (67; 2019) and "dexmedetomidine" (65; 2019) have been the main areas of interest in recent years. Conclusion: Research on preoperative anxiety in children has been the focus of increasing attention over the past fifteen years, with the majority of publications from high-income countries. This review provides a useful perspective for understanding research trends, hot topics, and research gaps in this expanding field.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.233 | 0.254 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".