The Role of Clinical Examination in Midface Volume Correction Using Hyaluronic Acid Fillers: Should Patients Be Stratified by Skin Thickness?
Bibliographic record
Abstract
Abstract Background Aesthetic physicians have several hundred injectable products to select from. Due to differences in their manufacturing technology, these products display varying biophysical qualities, such as their cohesivity and lift capacity. Currently, there is no guidance to objectively selecting the best product for a particular patient. Therefore, an algorithmic approach is required to take specific skin characteristics into consideration. Objectives To evaluate (1) whether subjects seeking injectable treatments for midfacial volume loss and/or contour deficiency can be stratified based on specific skin characteristics (eg, thickness, fat quantity, bony structure) and (2) whether particular hyaluronic acid fillers perform best when used in such particular strata. Methods This was a prospective, Phase IV, open-label, single-center clinical trial. Thirty female patients with midface/cheek volume loss and/or contour deficiency were recruited (mean age, 53.5 years; SD, 12.57; range, 35–75 years). Subjects were treated with either Restylane Lyft (HAL) or Restylane Volyme (HAV) and followed for 4 months post-injection. Treatment allocation was based on the treating physician’s clinical evaluation and compared with ultrasound evaluation. Ultrasound images were used to confirm stratification. Safety and efficacy assessments were performed at each study visit: baseline, week 2, week 4, week 8, and week 16. Subgroup analyses evaluated whether particular strata performed best when treated with specific products. Results The 2 investigative products varied in their efficacy, depending on the characteristics of the subject. Conclusions The use of a treatment algorithm may improve outcomes for patients seeking injectable treatments for midfacial volume loss and contour deficiencies. Level of Evidence: 2
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".