Defining Skin Quality: Clinical Relevance, Terminology, and Assessment
Bibliographic record
Abstract
BACKGROUND: Flawless skin is one of the most universally desired features, and demand for improvements in skin quality is growing rapidly. Skin quality has been shown to substantially impact emotional health, quality of life, self-perception, and interactions with others. Although skin quality improvements are a common end point in studies of cosmeceuticals, they are rarely assessed in clinical studies of other aesthetic treatments and products. Descriptive terminology for skin quality parameters also varies considerably within the aesthetic field, relying on a range of redundant and occasionally contradictory descriptors. In short, skin quality has not been clearly defined. OBJECTIVE: The goal of this review is to highlight the importance of skin quality to patients and physicians, explore known and unknown factors comprising skin quality, and provide clarity regarding terminology, descriptors, and evaluation tools for assessing skin quality. MATERIALS AND METHODS: A review of the literature on skin quality was performed without limitation on publication date. Relevant articles are presented. RESULTS AND CONCLUSION: We propose a framework of attributes contributing to skin quality rooted in 3 fundamental categories-visible, mechanical, and topographical-with the aim to provide information to help guide clinicians and inform future clinical studies.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".