Effectiveness of hyaluronic acid fillers for cheek augmentation using a treatment guide to choose between products
Bibliographic record
Abstract
Abstract Background Patients can have different reasons for seeking cheek augmentation; while some are in need of volume augmentation, others may request projection and lifting. A treatment guide can be useful for treating clinicians in choosing the most suitable product. Aims This 8‐week, multicenter study was conducted to evaluate the effectiveness and safety of cheek augmentation using a treatment guide to choose between study products HACON and HALYF. Patients/Methods Female subjects intending to undergo cheek augmentation were treated according to primary need for treatment—HACON for contouring or HALYF for projection. Treatments were performed according to approved labels. Assessments included Global Aesthetic Improvement Scale (GAIS) evaluations, subject satisfaction, subject‐perceived age (FACE‐Q), naturalness of facial expressions, 3D imaging analysis, and safety assessments. Results All subjects (n = 60) were assessed as aesthetically improved by the investigators 4 and 8 weeks after last injection. For all subjects, contouring or projection was achieved as planned with natural‐looking results. Subject satisfaction was high in both study groups. Volume change of the cheek area was statistically significant from baseline to Week 4 (p < 0.001), in both treatment groups and on both sides of the face. Overall, treatments were well tolerated with mainly mild adverse events related to treatment. Conclusions The proposed guide for product selection of HACON or HALYF for treatment of the cheek area was useful to achieve the primary treatment goal for both products. Treatments were well tolerated and associated with improved aesthetic appearance of the cheeks as well as high subject satisfaction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".