Three-dimensional biomodeling in complex mandibular reconstruction and surgical simulation: prospective trial.
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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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".