A global overview of the use of cone beam computed tomography in dentistry: a bibliometric review focusing on paediatric patients
Bibliographic record
Abstract
<ns3:p>Background Cone Beam Computed Tomography (CBCT) has improved diagnosis and treatment planning in paediatric dentistry, but no bibliometric studies have examined the research landscape. This study provides an overview of CBCT in paediatric dentistry. Methods A bibliometric review was conducted using articles from the Web of Science database up to February 2024. Conference papers and editorials were excluded. Data extracted included citation counts, publication dates, journals, impact factors, study designs, topics, geographical and institutional affiliations, authors, and keywords. Collaborative networks were visualised using VOSviewer, and Spearman’s correlation assessed the relationship between citation counts and other variables. Results The review analysed 517 articles, with the most cited receiving 557 citations. Publication dates ranged from 2005 to 2024, with a peak in 2023. Observational studies were the most common, particularly on maxillary expansion. The American Journal of Orthodontics and Dentofacial Orthopedics was the most cited journal, and the USA was a major contributor. Jacobs R authored the most articles (n=19), and the University of Alberta led in institutional output. Spearman’s correlation showed a weak positive correlation between citation count and journal impact factor (rho=0.272, p<0.001) and a strong negative correlation with publication year (rho=-0.762, p<0.001). Conclusions This bibliometric review provides an overview of the use of CBCT in paediatric dentistry, particularly in maxillary expansion. The findings suggest that more specific imaging protocols may improve safety and clinical outcomes, and that further investigation of long-term outcomes may provide valuable insights.</ns3:p>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.237 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".