Parametric Multi-Scale Modeling of the Zygomaticus Major and Minor: Implications for Facial Reanimation
Bibliographic record
Abstract
ABSTRACT: Facial paralysis can severely impact functionality and mental health. Facial reanimation surgery can improve facial symmetry and movement. Zygomaticus minor (Zmin) and zygomaticus major (Zmaj) are 2 important perioral muscles, that function to elevate the upper lip, contributing to the formation of a smile. The objective of this study was to analyze the morphology in three-dimensional (3D) and quantify architectural parameters of Zmin and Zmaj. In ten formalin-embalmed specimens, Zmin and Zmaj were serially dissected and digitized at the fiber bundle level. The 2 muscles were modeled in 3D to construct high fidelity models. The 3D models were used to assess muscle morphology and quantify architectural parameters including mean fiber bundle length, physiological cross-sectional area, and line of action. Zygomaticus minor fiber bundles were oriented horizontally or slightly obliquely and had a muscular attachment to the medial modiolus. Zygomaticus minor was found to either have no partitions or medial and lateral partitions. Specimens with partitions were divided into type 1 and type 2. Type 1 consisted of a medial partition with fiber bundles attaching to the zygomatic bone at the inferior margin of the orbit. The type 2 medial partition attached to the lateral margin of the orbit to attach to the zygomatic bone. Zygomaticus major had obliquely oriented fiber bundles with most specimens having inferior and superior partitions attaching to the inferior aspect of the zygomatic bone. Zygomaticus major was found to have a greater mean fiber bundle length and physiological cross-sectional area than Zmin. The direction of the line of action of Zmin and Zmaj was closely related to fiber bundle arrangement. Detailed 3D anatomical understanding of Zmin and Zmaj, at the fiber bundle level, is critical for reconstructive surgeons performing dynamic facial reanimation. This data can be used to assist with selecting the ideal donor site for reconstruction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".