The Accuracy of 3D Surgical Design and Simulation in Prefabricated Fibula Free Flaps for Jaw Reconstruction
Bibliographic record
Abstract
The ideal jaw reconstruction involves the restoration and maintenance of jaw continuity, jaw relations, joint alignment, and facial contour, and, most importantly, dental occlusal reconstruction. One of the essential requirements of achieving a consistent functional outcome is to place the bony reconstruction in the correct three-dimensional position as it relates to the other jaw segments and dentition. A protocol of occlusion-driven reconstruction of prefabricated fibular free flaps that are customized to the patient with surgical design and simulation (SDS)-planned osseointegrated implant installation was developed by our institution. This innovation introduced significant flexibility and efficiency to jaw reconstructions, but functional and cosmetic outcomes were dependent on the accuracy of the final reconstructions when compared to the SDS plan. The purpose of this study was to examine the accuracy of the SDS-planned fibular flap prefabrication in a cohort of patients undergoing jaw reconstruction. All patients that had undergone primary jaw reconstruction with prefabricated fibular free flaps were reviewed. The primary outcome of this study was the accuracy of the postoperative implant positions as compared to the SDS plan. A total of 23 implants were included in the analysis. All flaps survived, there was no implant loss postoperatively, and all the patients underwent all stages of the reconstruction. SDS planning of fibular flap prefabrication resulted in better than 2 mm accuracy of osteointegrated implant placement in a cohort of patients undergoing jaw reconstruction. This accuracy could potentially result in improved functional and cosmetic outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".