Bibliometric analysis of pediatric dental sedation research from 1993 to 2022
Bibliographic record
Abstract
Statement of problem: Bibliometric analysis methods were used to evaluate pediatric dental sedation research and to identify topical hotspots using quantitative and qualitative methodologies. Purpose: To conduct bibliometric analysis on the retrieved data and to foresee the development of trends and hotspots in this research area. Material and methods: We retrieved appropriate research articles from the Web of Science Core Collection on January 1, 2023. VOSviewer, Citespace and the Bibliometrics website were used to conduct bibliometric analysis on the retrieved data. GraphPad Prism 10.0 (GraphPad, San Diego, CA, USA) was used to conduct the statistical analysis. Results: A total of 396 publications on pediatric sedation in dentistry, published between 1993 and 2022, were retrieved from online databases. The USA published most papers. Furthermore, the most frequent countries who cooperated were the USA and Canada. Six of the top ten publishing establishments were USA based. Papers on the research have appeared primarily in the journals of Dentistry and Anesthesiology. Keyword co-occurrence and co-citation cluster analysis revealed that the most common topics mainly were: dental anxiety; conscious sedation; dental caries; midazolam; propofol; hypoxemia. Conclusions: During the three decades, the focus of pediatric sedation research has been on drugs, dental anxiety and procedural sedation. Keyword burst detection indicated that procedural sedation; adverse event; respiratory depression is an emerging research hotspot.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.199 | 0.276 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".