Current Concepts in Dental Trauma Management, Documentation, Follow‐Up and Education: Proceedings From the World Congress on Dental Traumatology ( <scp>WCDT</scp> )
Bibliographic record
Abstract
It is a great pleasure to present this special issue of Dental Traumatology, dedicated to current concepts in dental trauma management, documentation, follow-up and education. This issue is a result of the work of leading figures in the world of dental trauma and based on the presentations given at the World Congress on Dental Traumatology (WCDT) that was held in Tokyo, Japan, in July 2024. The issue provides a diverse overview of practical issues that would benefit all of us, who are working with dental trauma patients. The papers cover a wide range of topics starting with the proper documentation of traumatic dental injuries [1]. Documentation and record keeping are of utmost importance in the long-term handling of trauma cases [2, 3]. Well-informed documentation will help with progress and healing evaluation and can be valuable tool for communication with patients and colleagues. Globally, on a larger scale, this will enable better data collection and promotion of public policies and services [4, 5]. This issue further presents a thorough and multi-angle views on the management of injuries in primary and young permanent teeth [6-8]. These cases are often very challenging and require comprehensive understanding and multi-disciplinary approaches as well as a long-term plan for follow-up and identification of possible complications [9-12]. Some of the possible complications and the ways to mitigate and handle them are also an important portion of this special issue [13-15]. Additionally, in this issue, there are important reports on the interesting phenomenon of transient apical breakdown (TAB) that was not vastly investigated and explored yet [16] as well as the examination of new digital technology applications for auto-transplantation of teeth [17]. Various approaches had been suggested recently to improve the long-term outcomes of tooth transplantation, and the utilization of novel techniques and technologies have the potential to increase the predictability and success rates of this important treatment modality [18-23]. Finally, you will be able to find an up-to-date view on dental trauma education for the new generation of students and learners, a fascinating topic that will require attention in the next years in order to provide proper knowledge in effective ways to our next generation of oral health professionals [24]. Many papers had been published recently on the use of AI tools and online information to enhance dental trauma education and knowledge; however, these tools need to be used with caution to avoid misinformation and mal-informed decisions [25-31]. Education of the next generation both of dental professionals and the public is an extremely important responsibility and efforts should be continuously made to improve and enhance these educational practices [32-38]. The Dental Traumatology editorial team as well as the International Association of Dental Traumatology would like to thank all the authors for their time, efforts and great contribution to this special issue and to our field. The author takes full responsibility for this article. The author declares no conflicts of interest.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".