How to Treat Skin Quality: A Consensus‐Based Treatment Algorithm and Expert Guidance
Bibliographic record
Abstract
INTRODUCTION: Skin quality can be described using four emergent perceptual categories (EPCs): skin tone evenness, skin surface evenness, skin firmness, and skin glow. While the publication in which the EPCs were originally described by Goldie et al. notes possible treatments for each EPC, there remains a need for a resource to guide clinicians in treatment selection when addressing EPCs in clinical practice. METHODS: Twelve expert aesthetic physicians from across the globe participated in this EPC working group. A modified Delphi method was used to develop a treatment algorithm. First, panelists ranked a range of aesthetic treatments based on their ability to directly improve a given EPC. A draft algorithm was developed and evaluated during a moderated discussion. The treatment algorithm was then updated and reviewed by each individual participant, with the appropriateness of treatments listed for each EPC ranked using the RAND/UCLA scale. The algorithm was again updated based on this feedback and presented to the group for final review and approval during another virtual meeting. RESULTS: The treatment algorithm developed by the working group is presented here alongside clinical pearls and preferred approaches for managing each EPC. It is the hope of the working group that the algorithm can be applied in real-world clinical practice to improve patient skin quality and aesthetic outcomes. CONCLUSION: The presented guidance can serve as an approach to improving skin quality utilizing the framework of EPCs. The treatment algorithm may be applied to a range of skin types in practices across the globe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".