A <scp>3D</scp> Analysis of Plating Strategies in Mandibular Reconstruction: A Randomized Control Pilot Study
Bibliographic record
Abstract
OBJECTIVE(S): The purpose of this study was to compare computer-assisted mandibular plating to conventional plating using quantitative metrics. METHODS: Patients scheduled to undergo mandibular reconstruction were randomized to three-dimensional modelling for preoperative plate bending or intraoperative freehand bending. Preoperative and postoperative head and neck computed tomography scans were obtained to generate computer models of the reconstruction. The overall plate surface contact area, mean plate-to-bone distance, degree of conformance, and position of the condylar head within the glenoid fossa between pre- and post-operative scans were calculated. RESULTS: Twenty patients were included with a mean age of 57.8 years (standard deviation [SD] = 13.6). The mean follow-up time was 9.8 months (range = 1.6-22.3). Reconstruction was performed with fibular (25%) or scapular free flaps (75%). The percentage of surface contact between the reconstructive plate and mandible was improved with three-dimensional models compared to freehand bending (93.9 ± 7.7% vs. 78.0 ± 19.9%, p = 0.04). There was improved overall plate-to-bone distance (3D model: 0.7 ± 0.31 mm vs. conventional: 1.3 ± 0.8 mm, p = 0.06). Total intraoperative time was non-significantly decreased with the use of a model (3D model: 726.5 ± 89.1 min vs. conventional: 757.3 ± 84.1 min, p = 0.44). There were no differences in condylar head position or postoperative complications. CONCLUSION: Computer-assisted mandibular plating can be used to improve the accuracy of plate contouring. LEVEL OF EVIDENCE: 2 Laryngoscope, 134:2182-2186, 2024.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".