Sensitivity Analysis of a Validated Subject-Specific Finite Element Model of the Human Craniofacial Skeleton
Bibliographic record
Abstract
Developing a more complete understanding of the mechanical response of the craniofacial skeleton (CFS) to physiological loads is fundamental to improving treatment for traumatic injuries, reconstruction due to neoplasia, and deformities. Characterization of the biomechanics of the CFS is challenging due to its highly complex structure and heterogeneity, motivating the utilization of experimentally validated computational models. As such, the objective of this study was to develop, experimentally validate, and parametrically analyse a patient-specific finite element (FE) model of the CFS to elucidate a better understanding of the factors that are of intrinsic importance to the skeletal structural behaviour of the human CFS. An FE model of a cadaveric craniofacial skeleton was created from subject-specific computed tomography data. The model was validated based on bone strain measurements taken under simulated physiological-like loading through the masseter and temporalis muscles (which are responsible for the majority of craniofacial physiologic loading due to mastication). The baseline subject-specific model using locally defined cortical bone thicknesses produced the strongest correlation to the experimental data (r2 = 0.73). Large effects on strain patterns arising from small parametric changes in cortical thickness suggest that the very thin bony structures present in the CFS are crucial to characterizing the local load distribution in the CFS accurately.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".