Consensus Recommendations for Combined Aesthetic Interventions in the Face Using Botulinum Toxin, Fillers, and Energy-Based Devices
Bibliographic record
Abstract
BACKGROUND: The aging process is a complex interplay of intrinsic and extrinsic factors across multiple layers of the face. Accordingly, combining aesthetic interventions targeting different manifestations of aging often leads to better results than single modalities alone. However, no guidelines for a pan-facial approach using multiple interventions have been published to date. OBJECTIVE: To develop consensus recommendations for the optimal combination and ideal sequence of botulinum toxin (BoNT), hyaluronic acid, calcium hydroxylapatite, and microfocused ultrasound with visualization (MFU-V) in persons of all Fitzpatrick skin types. METHODS AND MATERIALS: 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: Optimal aesthetic treatment of the face begins with a thorough patient assessment and an individualized treatment plan. Spacing consecutive treatments 1 to 2 weeks apart allows for resolution of side effects and/or to assess results. For same-day treatments, BoNT and fillers may be performed together in either sequence, whereas MFU-V is recommended before injectable agents. CONCLUSION: Expert consensus supports a combination approach using multiple modalities in specific sequence for the safe and effective treatment of the aging face.
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 | 0.089 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".