Three-dimensional biomodeling in complex mandibular reconstruction and surgical simulation: prospective trial.
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Mandibular reconstruction is challenging for experienced and resident surgeons. Three-dimensional (3D) biomodeling creates accurate physical models of patients' craniofacial skeletons, which can potentially assist reconstruction. However, this capacity has not been objectively examined. OBJECTIVE: The purpose of this study was to assess 3D biomodels in performing and learning mandibular reconstruction through surgical simulation. DESIGN: Prospective cohort study. SETTING: Tertiary care academic referral centre. METHODS: Ten experienced and 10 naive resident surgeons were asked to bend and fixate a titanium reconstruction plate, for a standardized anterior hemimandibular defect, on a 3D biomodel by freehand or 3D biomodel-assisted means. Participants were randomized to which technique was performed first. Twenty-four to 48 hours later, participants performed the opposite technique. MAIN OUTCOME MEASURES: Accuracy was measured by anterior mental projection and intercondylar and interangular splay. The results per technique were compared to a complete (control) mandible. The time of reconstruction and usability of each technique, as per an International Standards Organization-based questionnaire, were also determined. RESULTS: Three-dimensional biomodel-assisted reconstruction led to plates with statistically indifferent projection and splay compared to the control (p < .05) for both groups. Conversely, freehand constructs significantly deviated in projection and splay for either group (p < .05). No difference in reconstruction time by technique was found (p < .05). Usability favoured 3D biomodel-assisted bending, with significantly higher ratings in either group (p < .05). CONCLUSIONS: Three-dimensional biomodels provide a usable and accurate means of mandibular reconstruction for experienced surgeons. Moreover, when used in surgical simulation, they provide an effective tool for teaching residents.
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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 it