Optimizing Injectable Poly-L-Lactic Acid Administration for Soft Tissue Augmentation: The Rationale for Three Treatment Sessions
Bibliographic record
Abstract
BACKGROUND: The availability and variety of different injectable modalities has led to a dramatic increase in soft tissue augmentation procedures in recent years. Injectable poly-L-lactic acid (PLLA) is a synthetic, biodegradable polymer device approved in the United States for use in immunocompetent patients as a single regimen of up to four treatment sessions for correction of shallow to deep nasolabial fold contour deficiencies and other facial wrinkles. Injectable PLLA is also approved for restoration and/or correction of signs of facial fat loss (lipoatrophy) in individuals with HIV. METHODS: The present article provides an overview of previous studies with injectable PLLA, and specifically focuses on the number of recommended treatment sessions and intervals between treatment sessions. The authors also provide two case studies to support their recommendations for an average of three treatment sessions. RESULTS: Although the specific mechanisms remain hypothetical, injections of PLLA are believed to cause a cascade of cellular events that lead to collagen repair and subsequent restoration of facial volume. Because the development of a response to injectable PLLA is gradual and its duration of effect is long lasting, sufficient time between treatment sessions should be allocated to avoid overcorrection. CONCLUSION: Studies of injectable PLLA support the hypothesized mode of operation, and the experience and clinical recommendations of the authors that suggest that three treatment sessions are an optimal regimen for use of injectable PLLA in the majority of patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".