A Randomized Controlled Trial Evaluating Traditional Versus Extended Techniques of Poly-l-Lactic Acid Injection for the Aesthetic Improvement of the Temporal Fossae
Bibliographic record
Abstract
BACKGROUND: Poly- l -lactic acid (PLLA-SCA) can be used to improve the aesthetics of the temporal fossae but is usually only deposited within the visible area of the temporal hollows. However, extending the treatment area posterior to the hairline may provide a greater lifting effect. OBJECTIVES: Assess the efficacy and safety of 2 techniques (traditional vs extended) of injecting PLLA-SCA for aesthetic improvement of the temporal fossae. MATERIALS AND METHODS: A prospective, randomized-controlled clinical trial was conducted. Twenty-four subjects were randomized in a 1-to-1 ratio to the traditional or extended injection technique. Subjects received 3 treatments (Baseline, Week 7, Week 14) and were evaluated for follow-up at Weeks 20, 26, and 38. Assessments included the Global Aesthetic Improvement Scale (GAIS), Temple Volume Deficit Scale (TVDS), lifting and volumizing effects, subject satisfaction, and adverse events. RESULTS: A greater facial lifting effect ( p < .001) and mean decrease in TVDS scores were observed with the extended injection technique. Regardless of injection technique, all subjects were at least improved on the GAIS at Week 38, and there was a low frequency of adverse events. CONCLUSION: Poly- l -lactic acid is a safe and effective treatment for improving the aesthetic appearance of the temporal fossae. Outcomes can be maximized using an extended injection technique.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".