Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,170 | 0,049 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».