Evolution of Superficial Muscular Aponeurotic System Facelift Techniques: A Comprehensive Systematic Review of Complications and Outcomes
Bibliographic record
Abstract
Background: Facelift procedures are a popular method of facial rejuvenation. The most common technique is superficial muscular aponeurotic system (SMAS) plication, with several variations. However, the optimal approach remains unclear. This review analyzed previous studies to compare SMAS facelift techniques, their outcomes, and complication rates. Methods: A systematic search was conducted using the MEDLINE, Cochrane, Embase, and Google Scholar electronic databases in September 2022. The search included studies published from January 2000 to September 2022 using keywords such as "facelift," "complications," and "outcomes." Results: This review examined 27 selected studies that evaluated 6 SMAS facelift techniques. The studies involved 6086 patients in total, over 85% of who were satisfied with the outcome of their surgery. The complication rates varied depending on the technique used, with the SMAS flap and composite SMAS technique having the highest (5.75%) and lowest (0.05%) complication rates, respectively. The most common complications were temporary facial nerve injury (0.85%) and skin necrosis (0.41%). To date, only one case of permanent facial nerve injury has been reported. Conclusions: On the basis of our findings, SMAS facelift techniques achieve high patient satisfaction rates, with complication rates that vary by technique. The composite SMAS technique showed the lowest complication rates, whereas the SMAS flap showed the highest rate. However, some studies have not reported all complications, making it difficult to determine the best approach. Therefore, future studies are required to identify the most aesthetically pleasing technique with the lowest complication risk.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".