To What Extent Can Mastication Functionality Be Restored Following Mandibular Reconstruction? A Simulation Study
Bibliographic record
Abstract
Abstract Advanced cases of head and neck cancer involving the mandible often require surgical removal of diseased sections and subsequent replacement with donor bone. During the procedure, the surgeon has to decide which soft tissues structures to resect (especially muscles). This requires balancing tradeoffs related to issues such as surgical access and post-operative function; however, the latter is often difficult to predict, especially given that long-term functionality also depends on the impact of post-operative rehabilitation programs. To assist in surgical decision-making, we present an approach to estimating effects of typical muscle resections on key aspects of post-operative function. We validate our dynamic biomechanical models of the reconstructed mandible using literature data and use these models to estimate the degree of functionality that might be achieved following post-operative rehabilitation. We find significant potential for restoring mandibular functionality, even in cases involving large defects. This entails an average trajectory error below 2 mm, bite force comparable to a healthy individual, improved condyle mobility, and a muscle activation change capped at a maximum of 20%. These results suggest that there is significant potential for improving post-operative rehabilitation to gain greater restoration of jaw function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".