Advances in Facial Rejuvenation: Botulinum Toxin Type A, Hyaluronic Acid Dermal Fillers, and Combination Therapies???-Consensus Recommendations
Bibliographic record
Abstract
BACKGROUND: Facial aesthetics and rejuvenation are evolving rapidly due to changes in products, procedures, and patient demographics. Clinicians can benefit from ongoing guidance on products, tailoring treatments to individual patients, treating multiple facial areas, and using combinations of products and ways to optimize outcomes. METHODS: A multidisciplinary group of aesthetic treatment experts convened to review the properties and uses of botulinum toxin type A (BoNTA) and hyaluronic acid fillers and to update consensus recommendations for facial rejuvenation using these two types of products. The group considered paradigm shifts in facial aesthetics; optimal techniques for using BoNTA and hyaluronic acid fillers alone and in combination; the influence of patient sex, ethnicity, cultural ideals, and skin color on treatment; general techniques; patient education and counseling; and emerging trends and needs in facial rejuvenation. RESULTS: The group provided specific recommendations by facial area, focusing on relaxing musculature, restoring volume, and recontouring using BoNTA and hyaluronic acid fillers alone and in combination. For the upper face, BoNTA remains the cornerstone of treatment, with hyaluronic acid fillers used to augment results. These fillers are central to the midface because of the need to restore volume. BoNTA and hyaluronic acid in combination can improve outcomes in the lower face. CONCLUSIONS: Optimal outcomes in facial aesthetics require in-depth knowledge of facial aging and anatomy, an appreciation that rejuvenation is a three-dimensional process involving muscle control, volume restoration, and recontouring, and thorough knowledge of properties and techniques specific to each product in the armamentarium.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".