Consensus Recommendations for Combined Aesthetic Interventions Using Botulinum Toxin, Fillers, and Microfocused Ultrasound in the Neck, Décolletage, Hands, and Other Areas of the Body
Bibliographic record
Abstract
BACKGROUND: The popularity of aesthetic procedures in the face has led to greater disparity between treated areas and those that still show evidence of true age. Although many areas of the body often require multiple treatment procedures for optimal rejuvenation, combination therapy for specific areas is not yet well defined. OBJECTIVE: To develop recommendations for the optimal combination and ideal sequence of botulinum toxin (BoNT), hyaluronic acid, calcium hydroxylapatite (CaHA), and microfocused ultrasound with visualization in nonfacial areas across all skin phototypes. METHODS: Fifteen specialists convened under the guidance of a certified moderator. Consensus was defined as approval from 75% to 94% of all participants, whereas agreement of ≥95% denoted a strong consensus. RESULTS: Recommendations have been provided for the neck, décolletage, and hands and include the timing and sequence of specific procedures when used concurrently or over several treatment sessions. Position statements are offered in lieu of consensus for the upper arms, abdomen, buttocks, and knees. CONCLUSION: Nonfacial rejuvenation often requires multiple procedures for optimal results in individuals with significant age-related changes. Further clinical studies are recommended to raise awareness of non-facial indications and provide clinicians with the best evidence for best treatment practices.
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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 | 0.083 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".