Modified Composite-Flap Facelift Combined With Finger-Assisted Malar Elevation (FAME): A Cadaver Study
Bibliographic record
Abstract
BACKGROUND: Inadequate release of retaining ligaments during facelift surgery may lead to an unnatural appearance. However, most facelift surgeons are hesitant in transecting these ligaments to avoid possible injury to facial subbranches. OBJECTIVES: In the authors' surgical practice for modified composite flap rhytidectomy, the authors employed the finger-assisted malar elevation (FAME) technique in order to enable safe release of the zygomatic cutaneous ligaments through the prezygomatic space under direct vision. The aim was to evaluate the anatomical basis and safety measures of this technique through a cadaveric dissection study. METHODS: Modified composite-flap facelift with the FAME technique was carried out in 22 fresh cadaver hemi-faces. All facial nerve subbranches were dissected thoroughly to assess for any evidence of injury during facelift, and to evaluate the safety of the operation. The relations among the facial nerve, zygomatic cutaneous and masseteric ligaments, orbicularis oculi muscle, and malar fat pad were investigated. RESULTS: Finger dissection of the prezygomatic space allows safe release of the zygomatic cutaneous ligaments as well as adequate entry to a proper surgical plane above the zygomatici muscles under direct vision, while leaving the malar fat pad and overlying structures attached to the skin without the need of a transblepharoplasty approach. CONCLUSIONS: This study by the authors shows that a modified composite-flap facelift with FAME technique is a safe procedure that allows adequate and effective repositioning of an en-bloc composite flap that produces balanced and harmonious rejuvenation of the midface and lower face without the need of a separate midface lift.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".