Combined Application of Virtual Simulation Technology and 3-Dimensional-Printed Computer-Aided Rapid Prototyping in Autotransplantation of a Mature Third Molar
Bibliographic record
Abstract
The use of computer-aided rapid prototyping (CARP) models was considered to reduce surgical trauma and improve outcomes when autotransplantation of teeth (ATT) became a viable alternative for dental rehabilitation. However, ATT is considered technique-sensitive due to its series of complicated surgical procedures and unfavorable outcomes in complex cases. This study reported a novel autotransplantation technique of a 28-year-old patient with an unrestorable lower first molar (#36) with double roots. Regardless of a large shape deviation, a lower third molar (#38) with a completely single root formation was used as the donor tooth. ATT was performed with a combined use of virtual simulation, CARP model-based rehearsed surgery, and tooth replica-guided surgery. A 3D virtual model of the donor and recipient site was generated from cone-beam computed tomographic (CBCT) radiographs prior to surgery for direct virtual superimposition simulation and CARP model fabrication. The virtual simulation indicated that it was necessary to retain cervical alveolar bone during the surgical socket preparation, and an intensive surgical rehearsal was performed on the CARP models. The donor tooth replica was used during the procedure to guide precise socket preparation and avoid periodontal ligament injury. Without an additional fitting trial and extra-alveolar storage, the donor tooth settled naturally into the recipient socket within 30 s. The transplanted tooth showed excellent stability and received routine root canal treatment three weeks post-surgery, and the one-year follow-up examination verified the PDL healing outcome and normal functioning. Patient was satisfied with the transplanted tooth. This cutting-edge technology combines virtual simulation, digital surgery planning, and guided surgery implementation to ensure predictable and minimally invasive therapy in complex cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".