Autologous Fat Injection for Augmentation Rhinoplasty: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Autologous fat has become more frequently used for nasal volume augmentation and nasal correction. Nasal lipofilling refers to the use of injectable autologous fat grafts for nonsurgical aesthetic corrections. OBJECTIVES: This systematic review aims to assess the satisfaction, complication, and retention rates of fat injection in nasal shape corrections. METHODS: The authors searched PubMed/Medline and Google Scholar up to and including October 2020 with no time and language restrictions for pertinent materials. Two authors conducted a duplicate searching process independently to determine proper materials based on the inclusion and exclusion criteria. One author retrieved the following data from the finally included studies based on a predefined checklist worksheet. RESULTS: The included studies report data from a total of 564 patients undergoing nasal fat injection in 12 studies. The mean score in our included materials was 6.08 with a range of 4 to 7 scores. In most of our included materials, no complication was reported for the peri/postsurgical period. Although some papers reported manageable complications such as an insufficient volume or decreased volume by resorption, tip excess and supratip fillness, and mild displacement, more than half of our included materials reported on patient satisfaction with aesthetic results of fat injection. The satisfaction rates were mostly high and ranged from 63% to 100%. CONCLUSIONS: Autologous fat injection is an effective and minimally invasive treatment for nasal aesthetic and contour correction with a high satisfaction rate and low complication rate. Clinical expertise is essential to have a safe injection and to minimize the potential complications.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".