Fabrication and evaluation of a novel patient-specific 3D-printed simulation model for oral surgical training
Bibliographic record
Abstract
BACKGROUND: Use of patient-specific models as a surgical planning and training tool can support novice practitioners' surgical skill development. This study aimed to introduce a novel workflow for fabricating 3D-printed, patient-specific simulation models and evaluate their accuracy and transferability for use in periodontal and oral surgery training. METHODS: Patient-specific anatomical models of the maxilla were fabricated using the CBCT and intraoral scan data. The proposed workflow outlines a novel process for creating a patient-specific model that accurately replicates both the hard and soft tissues of the patient. The accuracy of the printed models was evaluated by scanning five models and comparing them to the patient's intraoral scan using cloud-to-cloud distance analysis. Then, in an exploratory study design, a simulated gingival flap surgery exercise was completed by 18 periodontists and 50 students. The face and content validity of the model were assessed using an 8-item online questionnaire with a VAS of 0-100 and a free comment question. The data were analyzed using descriptive statistics, Mann-Whitney U tests and independent t-test. RESULTS: The printed model demonstrated high dimensional accuracy. The overall VAS score of the model was significantly higher for students than for periodontists (83.7 ± 9.7 vs. 72.1 ± 15.8, p < 0.006). The face and content validity scores reported by students were also higher (P < 0.01), with mean differences of 8.86% and 12.62%, respectively. Periodontists rated the models lower for soft-tissue tactile feedback, particularly during incision. CONCLUSIONS: The proposed 3D-printed simulation workflow produced an accurate and educationally valuable model with the potential to enhance surgical training. Experienced surgeons suggested that refining the soft-tissue realism could further improve its overall educational value.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".