Trends in orthodontic scientific contributions: An evaluation based on the American Association of Orthodontists annual sessions
Bibliographic record
Abstract
Scientific contributions (lectures and posters) to the American Association of Orthodontists (AAO) annual sessions from 2013 to 2023 were investigated with the aims of analysing the contributions of each country and their efficiency, presentation trends, and gender differences during these years as well as the most frequent topics and their evolution. Official data were requested from and provided by the AAO secretary. The year and type of presentation; the name, country and gender of the first author; and the full title of the presentation were considered. In addition, six national indicators that could determine the quantity and quality of scientific production were obtained from the Our World in Data website with regard to the countries that made the greatest contributions to the AAO annual sessions. The USA featured the largest number of lecturers (69.44%), while the presentations of posters were more balanced among the 4 countries that exhibited the highest levels of production (i.e., Brazil, the USA, Mexico and South Korea). Brazil was the main country to perform above expectations. The COVID-19 pandemic resulted in a significant reduction in the number of poster presentations. The male/female ratio was close to 3:1 in terms of lectures and close to 1:1 in terms of posters. In 2023, women presented more posters than did men. The terms clear/aligners and digital were strongly present, and the terms maxillary, adults, and expansion were used increasingly frequently, while the use of the terms brackets or cephalometry decreased. American lecturers included terms that differentiated them from lecturers in other countries. The nationalities of lecturers are not closely related to those of posters, particularly with regard to the USA, Brazil, Canada, Mexico and Turkey. Research spending and economic level are the most significant factors with respect to the type and number of a country's contributions. Concerning gender, a clear imbalance in favour of men persists among lecturers. Increased distance from the USA makes it more difficult for women to serve as lecturers. An emergent paradigm shift in current topics towards a focus on the terms clear/aligners and digital in lectures is evident.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".