Comparison of student’s perceptions between 3D printed models versus series models in paediatric dentistry hands‐on session
Bibliographic record
Abstract
INTRODUCTION: Dental education emphasises the acquisition of technical skills. Recent advances in 3D printing technologies have enabled the emergence of new educational tools usable in hands-on work sessions. The possibility to print 3D models from CT scans of patients is now available to dental practitioners. The aim of this study was to develop and evaluate a 3D printed model for paediatric dentistry training and compare it to the reference model used in our faculty. MATERIALS AND METHOD: 3D models were obtained by modifying and printing the CT scan of a young patient using the Voco® Solflex 350 3D® printer and Voco® V-print resin. Thirty-four students were asked to perform a pulpotomy and preparation for a stainless steel paediatric crown on tooth 85 on both the 3D printed model and the industrial model (Frasaco®), and then to answer a questionnaire. The data were analysed using R software. RESULT: Both models obtained high scores. The learning potential and its applicability to clinical practice showed no statistically significant difference. Although the colour and the simulation of the proximal area disturbed the students (P = 0.009), the 3D models were seen as a good idea (P = 0.012). When it came to model design, the students appreciated the simulation of caries on 3D models (P = 0.0001) and considered the use 3D of models as a more realistic experience (P = 0.017). DISCUSSION: Although this study has some limitations (number of participants, choice of the models to be compared), it constitutes the first attempt to compare students' perception of 3D and series models. It shows that 3D technology makes it possible to obtain models of similar quality while offering a more realistic experience. CONCLUSION: There are still many ways in which these models could be improved. For example, modifying the quality of resins could improve the milling sensation, and the design could be improved to achieve better contact points. Nevertheless, these 3D models offer the possibility to give the patient a more central place in the education of future practitioners.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".