Enhancing Oral Surgery Simulation: A Systematic Review of 3D‐Printed Patient‐Specific Models Compared to Traditional Animal Jaw Models for Presurgical Training
Bibliographic record
Abstract
OBJECTIVES: 3D-printed simulation models are emerging as novel tools in various medical education fields. This study aims to investigate the evidence on the efficacy of 3D-printed jaw models compared to traditional animal models for oral surgical skill training. METHODS: A comprehensive literature search was conducted up to June 2024 in Ovid Medline, Embase, Scopus, ProQuest, Epistemonikos, and ERIC databases. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, relevant studies were identified. Data were extracted independently by two reviewers. The Medical Education Research Study Quality Instrument tool (MERSQI) was used for methodological quality assessment. RESULTS: A total of 1074 potentially relevant publications were initially identified. only three articles met the stringent inclusion criteria. These studies provided unique insights into the application, effectiveness, limitations, and potential of 3D-printed versus animal models in dental oral surgery education, however, their methodological design received only a moderate score based on the MERSQI evaluation. In all three studies, the participants preferred 3D-printed models over traditional cadaveric models in terms of anatomical accuracy, educational value, and surgical simulation. However, limitations were identified, particularly in replicating realistic soft tissue sensations. CONCLUSIONS: 3D-printed models can provide a realistic and novel alternative tool to the animal jaw, enhancing the learning experience in oral surgical skill training of dental students. Despite the limitations of the available studies, integrating 3D printing technology into dental and oral surgery education shows promise for improving educational quality. Future well-designed studies are needed to strengthen the existing evidence on this topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".