Brazilian oral medicine and oral pathology: international scientific collaborations
Bibliographic record
Abstract
BACKGROUND: Research in Oral Medicine (OM) and Oral Pathology (OP) in Brazil has experienced remarkable growth, gaining international recognition. However, no analysis has evaluated the patterns of evolution of international partnerships and their role in advancing Brazilian research in these areas. This study analyzed collaborations between Brazilian and international researchers in OP/OM. MATERIAL AND METHODS: A bibliometric cross-sectional survey was conducted using data from Brazilian researchers in OP/OM, identified through the Brazilian Society of Stomatology and Maxillofacial Pathology. Researchers' curriculum on the Lattes platform were analyzed, and data on publications, citations, co-authorships, affiliations, and journals were collected from the Scopus database, focusing on international collaborations. Bibliometric analyses were performed using the Bibliometrix tool in R Studio and VOSviewer software. Statistical trends between decades were compared using the Kruskal-Wallis test. RESULTS: The sample included 229 researchers, most females (61.6%), affiliated with public institutions (78.9%), and with a mean time since achieving the PhD of 15.27 years. Almost half of the researchers (43.2%) had postdoctoral training, and among them 43.4% completed it abroad, mainly in the United States (USA), Canada, and the United Kingdom (UK). A total of 2,027 articles were analyzed, revealing a 10.53% annual growth in publications. The number of international collaborations significantly increased over decades (p < 0.001), with USA, UK, India, and Italy being the leading partners. Collaborative publications showed a significant rise in citations (42.61 per paper), mainly in high-impact journals. CONCLUSIONS: A steady increase in international collaborations in OP/OM was identified, especially with the USA and the UK. These partnerships have increased citations and publications in high-impact journals, which highlights their benefit and importance for research in these areas.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.033 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".