3D‑printed training model for periodontal splinting: a randomized preclinical study
Clinical Snapshot
PICO Framework
| P — Population | 43 fourth-year dental students at University of Wuerzburg |
| I — Intervention | 3D-printed training model with repositionable mobile maxillary anterior teeth for periodontal splinting practice |
| C — Comparator | Two different splinting materials (polyethylene ribbon vs pre-impregnated unidirectional glass-fibre bundle) in randomized cross-over design |
| O — Outcomes | Student perception scores (6-point ordinal scale), self-reported preparedness for splinting, geometric deviation of tooth positioning accuracy |
Bottom Line
This randomized cross-over study demonstrates that a low-cost 3D-printed training model significantly improves dental students' self-reported preparedness for periodontal splinting procedures. The intervention showed a clinically meaningful 3-point improvement on a 6-point preparedness scale (p < 0.001) while maintaining technical accuracy across different splinting materials. The cross-over design strengthens internal validity by using students as their own controls. However, the study is limited by its small sample size, single-center design, and lack of long-term follow-up. The model appears to offer a practical, reproducible solution for dental education programs seeking to enhance preclinical training. While promising, larger multi-center studies with control groups receiving traditional training methods would strengthen the evidence base for widespread adoption.
Key Findings
P Value: p < 0.001
Effect Size: 3-point improvement on 6-point ordinal scale (50% improvement)
Primary Outcome: Self-reported preparedness for periodontal splinting improved significantly from poor (Md 5.0, IQR 1.0) to good (Md 2.0, IQR 1.0)
Nnt Or Sensitivity: Not applicable for educational intervention study
Confidence Interval: Not reported
Clinical Application
High feasibility given low cost of 3D printing technology and widespread availability in dental schools Highly relevant to Australian dental education programs; aligns with ADEA competency standards for dental graduates and could enhance practical skills training before clinical placement Dental students in preclinical training programs, particularly those learning periodontal splinting techniques
Abstract
Preclinical simulation is essential for students to practice before clinical treatment. A low-cost 3D-printed model was developed with repositionable mobile maxillary anterior teeth to train periodontal splinting and evaluate learner perception and repositioning accuracy. In a two-period randomized cross-over course 43 students in their fourth year completed two palatal slot splints from the right to the left maxillary canine (FDI 13-23) in randomized material order. They used a polyethylene ribbon and a pre-impregnated unidirectional glass-fibre bundle. Outcomes included an electronic questionnaire using an anchored 6‑point ordinal scale from 1 for "Excellent" to 6 for "Unsatisfactory" following the German school grading system and geometric deviation of each tooth to an ideal position. The course was rated overall as good (Md 2.0, IQR 1.0) and highly clinically relevant (Md 1.0, IQR 1.0). Self-reported preparedness for splinting improved from poor (Md 5.0, IQR 1.0) before the course to good (Md 2.0, IQR 1.0; p < 0.001) after the course. In this pilot study, no statistically detectable positional deviations between materials or between the first and second session in this cohort were observed. This inexpensive, reproducible 3D-printed model enables realistic periodontal splinting training under simulated mobility conditions and may support structured preparation for clinical teaching.
References
- 1.Hoehne, C., Rehling, S., Schrenker, J., Jockel-Schneider, Y., Krastl, G., & Schmitter, M. (2026). 3D‑printed training model for periodontal splinting: a randomized preclinical study. Scientific Reports. https://doi.org/10.1038/s41598-020-70418-y
Related Research
International dental journal
Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis
2 Aug 2026
JMIR medical informatics
Large Language Models for Endodontic Symptom Assessment and Treatment Planning Using Image-Free Clinical Records: Comparative Evaluation Study
25 July 2026
JMIR medical education
Evaluating the Pediatric Behavior Guidance of Students Based on Actual Clinical Transcripts Scored by Faculty and Large Language Models: Pilot Comparative Study
14 June 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service