Hallmark of success: top 50 classics in oral and maxillofacial cone-beam computed tomography
Bibliographic record
Abstract
Purpose The aim of this study was to identify the top 50 cited articles on the use of cone-beam computed tomography (CBCT) for oral and maxillofacial applications and to summarise the characteristics of the most impactful research articles in this domain. Material and methods A database was generated by combining the search results from Thomson Reuters Web of Science and Elsevier’s Scopus to ensure that all top-cited publications were captured. We used three search fields to generate the database: 1) CBCT, 2) oral and maxillofacial pathologies, and 3) oral and maxillofacial anatomical structures. Publications were then ranked by citation counts and reviewed by two independent reviewers. Results A total of 50 top publications were included in the study. Their citation count ranged from 43 to 170 with a median of 55.5. Five publications were cited more than 100 times. All except for one paper were published after 2000. The most well published journal was the American Journal of Orthodontics and Dentofacial Orthopedics (n = 12), and the United States of America (n = 15) was the most productive country in the field. The majority of the studies (n = 27) discussed the imaging of primary tooth pathologies, but there are also a significant number of articles that discuss imaging of bone grafts or dental implants (n = 7), upper airways (n = 5), the skull (n = 4), and other maxillofacial structures (n = 7). Conclusions Our study identifies 50 research articles with the highest number of citations in oral and maxillofacial CBCT, discusses the characteristics and commonalities between these articles, and predicts future trends in the 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 | 0.011 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.073 | 0.072 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".