A Prospective Clinical Trial Evaluating the Efficacy and Safety of Non-Animal Stabilized Hyaluronic Acid Injections for Non-Surgical Rhinoplasty
Bibliographic record
Abstract
Background: Non-surgical rhinoplasty with hyaluronic acid (HA) filler is a three-dimensional reshaping technique that achieves tissue enhancement by placing HA deep to nasal skin. Due to its unique rheology, Restylane® Lyft (HA-L, Galderma, Uppsala, Sweden) may be particularly well-suited for injection rhinoplasty, as it has high gel firmness (G’) for strong structural support with minimal integration propensity. Methods: A prospective clinical trial was conducted to evaluate HA-L use for non-surgical rhinoplasty. Thirty-three females were observed over eight months, using the following schedule: Visit 1 = Baseline/Treatment 1; Visit 2 = Optional touch up (Week 2); Visits 3–6 = Follow-ups (Months 1,3,6,8). The primary endpoint was subject improvement at Month 1 assessed by a blinded evaluator using the Global Aesthetic Improvement Scale (GAIS). Subject satisfaction and adverse events (AEs) were also evaluated. Results: A deep, periosteal injection using a bolus technique and 0.34 cc of HA-L was most often used. Seven cases of positive aspiration occurred in 167 injection points (4.19%), among 6/33 (18.18%) subjects. Based on the GAIS, 100% of subjects met the primary endpoint. Subject satisfaction was maximal at Month 1 (100%) and largely maintained at Month 8 (78.57%). Besides expected injection-related AEs (eg, ecchymosis, erythema), immediate AEs during/following treatment were limited to presyncopal symptoms [5/33 subjects (15.15%)]. Importantly, no cases of ischemia were observed. Subject-reported AEs (eg, swelling, erythema, pain) dissipated within 2 to 7 days. Conclusion: Given the technical nature of this technique, HA-L may be well-suited for injection rhinoplasty, due to its strong safety and efficacy profile. Level of Evidence: Level III: Evidence obtained from well-designed cohort study.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".