OPTIMIZING BONE CUTS ENHANCES PREDICTED BONE UNION PROPENSITY IN MANDIBULAR BODY RECONSTRUCTION
Bibliographic record
Abstract
Reconstructing the mandible after the surgical removal of diseased sections is crucial for restoring functions such as chewing, speech, and facial integrity. However, achieving consistent bone union remains a significant challenge. This study hypothesizes that optimizing donor bone cuts, specifically through improved positioning and cut plane orientation, can enhance bone union outcomes. To test this hypothesis, we present a novel automated workflow that leverages Bayesian optimization. Applied to a mandibular body defect case, this approach integrates virtual reconstruction, remeshing, and biomechanical simulation, resulting in up to a 76% increase in predicted bone union propensity. Our findings indicate that donor cut planes warrant greater emphasis in surgical planning. Furthermore, automated optimization underscores the potential of this patient-specific, data-driven method to advance bone healing in mandibular reconstruction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".