Biomechanical Properties of the Facial Retaining Ligaments
Bibliographic record
Abstract
Osteocutaneous facial retaining ligaments play an important role in the aging face. We sought to better characterize the biophysical properties of these ligaments and, in doing so, provide an empirical basis for the natural descent seen in facial aging. Five fresh frozen cadaver heads yielding 10 hemifaces were dissected to expose the orbital, zygomatic, buccomaxillary, and mandibular osteocutaneous ligaments. Each ligament was assessed and subjected to biomechanical testing. The main outcome measures included ligament dimensions, stiffness, percentage of elongation, and force to initial and ultimate failure. Initial and ultimate failure testing revealed the zygomatic ligament to be strongest, followed by the orbital, mandibular, and maxillary ligaments. The zygomatic ligament was also stiffest, followed by the orbital, maxillary, and mandibular ligaments. The percentage of elongation acted as a surrogate marker of elasticity, with the greatest elasticity maintained by the mandibular ligament, followed by the orbital, zygomatic, and buccomaxillary ligaments. Ligament dimensions and biophysical properties did not vary relative to cadaveric hemiface, age, or sex. To our knowledge, this is the first investigation to quantify the biomechanical properties of the facial retaining ligaments. Inherent ligament properties seem to be related to the changes observed in facial aging, although further study is required. Brandt and coauthors sought to better characterize the biophysical properties of these ligaments and, in doing so, provide an empirical basis for the natural descent seen in facial aging.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".