Subject and partner satisfaction with lip and perioral enhancement using flexible hyaluronic acid fillers
Bibliographic record
Abstract
Abstract Background The injection of hyaluronic acid (HA) dermal fillers is a popular minimally invasive approach to improve lip volume and contour, and with improved techniques has gained popularity because full lips are often associated with beauty and youth. Patient satisfaction is a key driver for successful aesthetic procedures, influencing individual treatment plans and future recommendations. Objective To evaluate subject and partner satisfaction with the hyaluronic acid (HA) dermal filler HARK for lip enhancement at 8 weeks after the last treatment. Methods & materials Subjects in this open‐label study all received HARK in the lips, and an additional group also received HARR and/or HARD in nasolabial folds (NLFs) and marionette lines (MLs). Satisfaction was assessed at Weeks 4 and 8 after the last treatment using questionnaires (FACE‐Q™ [subjects] and KISSABILITY [subjects and partners]). Results Nineteen subjects received HARK only; 40 also received HARR and/or HARD. Subjects reported a high level of satisfaction with their lips following treatment. Increases from baseline in the mean total satisfaction score were statistically significant at Weeks 4 and 8 (P ≤ .001). Most subjects (≥89%) reported satisfaction on all FACE‐Q questions at Week 8. Both subjects and partners were satisfied with the kissability, appearance, and natural look and feel of the post‐treatment results. Conclusion This study demonstrated that HARK resulted in lip enhancement with high levels of subject and partner satisfaction, when used alone or in combination with HARR/ HARD in NLFs and MLs.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".