Combined aesthetic interventions for prevention of facial ageing, and restoration and beautification of face and body
Bibliographic record
Abstract
Abstract: The Merz Institute of Advanced Aesthetics Expert Summit was held in Prague, Czech Republic, from 19–20 November 2016. The meeting had a distinct advisory board character and invited aesthetic practitioners from all over the world to hear an international faculty present a range of keynote lectures and conduct live injection sessions with an emphasis on recent developments in combination aesthetic interventions for face and body rejuvenation and beautification. Aging is associated with changes in bones, muscles, ligaments, adipose tissue, and skin and, moreover, involves interactions among these tissue types. To achieve the most natural and harmonious rejuvenation of the face, all changes that result from the aging process should be corrected, which generally involves treatment with more than a single agent or technology. Presentations described innovative treatment algorithms for the face and body and focused on patients’ desires for natural-looking rejuvenation and how this requires a three-dimensional approach combining products that relax the musculature, volumize, and re-drape the skin. Besides treating the aging face, these procedures are increasingly used to enhance facial features as well as to delay facial aging in younger patients. The presentations covered patients from different ethnicities as well as the treatment of non-facial areas, with a particular focus on the use of Ultherapy ® for skin lifting and tightening, and new aesthetic procedures such as Cellfina ® and diluted Radiesse ® . The current report provides a summary of key presentations from the meeting. Keywords: calcium hydroxylapatite, Cellfina, hyaluronic acid, incobotulinumtoxinA, Ultherapy
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".