Bite Force Simulator: A Novel Technique to Simulate Craniofacial Strain In Vitro
Bibliographic record
Abstract
Existing in vitro simulators rely on external manipulation of the skull to replicate masticatory forces; however, external manipulations do not accurately represent internal loads as in physiological muscle forces.The purpose of the project is to develop an in vitro simulator that internally replicates the forces of mastication. The simulator has 3-dimensional-printed piston mounts that are reverse-engineered using a computed tomography scan of the specimen. The mounts are attached to the skull at muscle attachment sites using adhesive. The pneumatic pistons are sutured to muscle tendons; when the pistons are activated, they pull on the tendons which proportionally replicate muscle loads. The force output of the pistons can be individually modified by a custom software. Strain gauges are attached to craniofacial bones to measure deformation under replicated muscle loads. A 6 degrees-of freedom force sensor is placed intraorally to measure the generated bite force.The methodology was validated on 6 fresh-frozen cadaveric heads. Change in strain measurements was observed with change in simulated muscle loads. The simulator can validate computer simulation models and provide an experimental platform for craniofacial and dental implants. It sets the framework for a new, more physiologically consistent way of studying craniofacial stresses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".