In Vitro Quantification of Strain Patterns in the Craniofacial Skeleton Due to Masseter and Temporalis Activities
Bibliographic record
Abstract
Many complications in craniofacial surgery can be attributed to a lack of characterization of facial skeletal strain patterns. This study aimed to delineate human midfacial strain patterns under uniform muscle loading. The left sides of 5 fresh-frozen human cadaveric heads were dissected of all soft tissues except the temporalis and masseter muscles. Tensile forces were applied to the free mandibular ends of the muscles. Maxillary alveolar arches were used to restrain the skulls. Eight strain gauges were bonded to the surface of the midface to measure the strain under single muscle loading conditions (100 N). Maxillary strain gauges revealed a biaxial load state for both muscles. Thin antral bone experienced high maximum principal tensile strains (maximum of 685.5 με) and high minimum principal compressive strains (maximum of -722.44 με). Similar biaxial patterns of lower magnitude were measured on the zygoma (maximum of 208.59 με for maximum principal strains and -78.11 με for minimum principal strains). Results, consistent for all specimens and counter to previously accepted concepts of biomechanical behavior of the midface under masticatory muscle loading, included high strain in the thin maxillary antral wall, rotational bending through the maxilla and zygoma, and a previously underestimated contribution of the temporalis muscle. This experimental model produced repeatable strain patterns quantifying the mechanics of the facial skeleton. These new counterintuitive findings underscore the need for accurate characterization of craniofacial strain patterns to address problems in the current treatment methods and develop robust design criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".