Combination Therapy With Poly D–L Lactic Acid and Autologous Fat Transplantation for Infraorbital Rejuvenation
Bibliographic record
Abstract
BACKGROUND: Multiple approaches exist for treating infraorbital hollowing, including hyaluronic acid fillers and autologous fat transfer. However, traditional single-modality treatments often present limitations such as the Tyndall effect or unpredictable fat retention rates. OBJECTIVE: To evaluate the efficacy and safety of a novel combination therapy using poly-D,L-lactic acid (PDLLA) with autologous fat transplantation for infraorbital rejuvenation. METHODS: Eight patients (8 females) with varying presentations of infraorbital hollowing were treated with a combination of PDLLA and autologous fat transfer. Two formulations of PDLLA were utilized: a standard formulation for dermal injection and a volume formulation for subdermal placement. Fat was harvested using manual aspiration, processed via centrifugation (3000 rpm for 3 min), and injected using a systematic multilayer approach. Follow-up periods ranged from 6 to 14 months. RESULTS: All patients demonstrated significant improvement in infraorbital hollowing and skin quality. The average volume of harvested fat ranged from 20 to 35 mL, with bilateral injection volumes of 1.8 to 3.2 mL. Treatment outcomes showed enhanced fat graft retention and improved skin texture across all cases. No significant complications were observed, and patient satisfaction was consistently high. CONCLUSIONS: The combination of PDLLA with autologous fat transfer represents an effective approach for infraorbital rejuvenation, demonstrating superior outcomes compared with single-modality treatments. This technique offers improved fat graft retention, enhanced skin quality, and natural-looking results with a favorable safety profile.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".