Statistical evaluation of scientific publications in dentistry in America, in the last 25 years
Bibliographic record
Abstract
Introduction: Scientific publications in dentistry are of interest to know the advances that occur in this area of knowledge, in order to implement them for the good of the community. Objective: To compare the scientific production and the number of citations among American countries, related to the area of dentistry in the last twenty-five years. Methods: The information was extracted from the Scimago Journal & Country Rank portal, the variables evaluated were: total number of documents, cited documents, citations, self-citations, citations per document and h-index. The statistical methods used for data processing were: MANOVA multivariate analysis of variance, multidimensional canonical contrast test, and frequency statistics. Results: A significant statistical difference was detected between nations of the American continent (p<0.05), the United States occupies the first place in the largest number of publications and citations in the time period evaluated, followed by Brazil and Canada, Honduras does not register scientific contribution in the area of dentistry. Conclusion: The topic with the least frequency of publications is oral hygiene, while the miscellany of articles related to various topics in the field of dentistry, are those with the greatest impact both in the number of citations and publications. The areas of periodontics and orthodontics present a similar pattern over time. In general, there is a large gap between the nations of Central and South America with respect to North America.
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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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.026 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".