Autologous Fat Injection for Augmentation Rhinoplasty: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it