Bibliographic record
Abstract
Complete dentures have been one of the most common clinical procedures in dentistry throughout history. Teaching the fundamentals and the clinical aspects of complete dentures is crucial for training our future oral health care providers [1]. The educational approach for digital complete dentures in clinical setting has been a challenge for multiple reasons including students and instructors’ calibration, anatomical variations within cases and experience with using intraoral scanners [2]. All of those concerns, limited the exposure for our students with digital denture in clinical settings in their final years of their dental education [3]. Over the last 5 years, there have been great advances in the digital denture technology and its clinical efficiency which reduced the number of clinical visits for the conventional complete denture fabrication from 6–8 visits to 2–3 visits for the digital denture workflows [4, 5]. Considering all the advantages of the digital denture workflow, it is highly crucial to ensure that our students have proper experience with this aspect. In order to overcome the formerly described challenges, the digital denture education was introduced in a preclinical setting which allows our students and instructors to get the exposure to the digital denture workflow in a controlled environment. The preclinical manikin heads were used to mimic a clinical situation with maxillary and mandibular edentulous typodonts as seen in Figure 1. Thereafter, a Planmeca intraoral scanner was used for a virtual impression which was analyzed with the Planmeca CAD/CAM software. In Figure 2, the virtual casts of the upper and lower edentulous arches are shown. Thereafter, the students were shown the steps for virtual teeth set-up as shown in Figure 3. In this workflow, our predoctoral students were exposed to using an intraoral scanner to scan an edentulous arch as well as virtual teeth set-up for complete denture. The limitation of capturing the border extensions with the intra-oral scanning of an edentulous arch was clearly explained to the students. The need for using custom tray or 3D denture base prototype for border molding and proper capture of the extensions were explained. It was explained to the students that the digital denture workflow can be a hybrid workflow that is based on the different clinical presentations. In the preclinical setting, the goal was to expose the students to most of the basics of this workflow to mimic clinical scenarios. The application of the digital denture education in a preclinical setting allows our students to have a great experience and exposure with the application of the intraoral scanner, the virtual final impression for edentulous arch, the virtual articulation and finally the virtual teeth set-up. Moving forward with the 3D-printing of the teeth try-in and then the complete denture prototype. This also allows our students to experience the advantages and the limitations of digital dentures in a simulated environment before applying it in clinical settings. Overall, this empowers our students with the technology required so they are well prepared to apply it in clinical settings. The challenges imposed, such as mobile muscles in the floor of the mouth and the tongue movements, in utilizing an intraoral scanner clinically for edentulous mandible are clearly explained to the students. We are trying out the techniques described on a small cohort as a clinical elective course as a proof of concept before making changes to the curriculum and applying it in the predoctoral clinical care. The authors extend their acknowledgment to Arvin Bagheri, UBC, Vancouver, Canada and Daniel Song, Vancouver, Canada for the laboratory support. The authors declare no conflicts of interest.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.170 | 0.049 |
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".