Treatment of acne in the aesthetic patient: A round table update
Bibliographic record
Abstract
BACKGROUND: The popularity of social media appears to be increasing the acceptance of cosmetic treatments, prompting more consumers to seek cosmetic treatments. As the estimated prevalence of acne vulgaris among adult women may be as high as 54%, acne is commonly observed among patients presenting for cosmetic treatments. Concomitant treatment of acne in the aesthetic patient population will improve overall clinical outcomes. AIMS: The goal of this work was to deliver a high-quality ethical and evidence-based educational program to physicians and adjunctive health care providers to advance patient care. METHODS: This paper is based on a webcam presentation with roundtable discussion by several notable experts in their field. RESULTS: A range of topical medications, injectable products, chemical peels, and energy-based devices are available for treating acne vulgaris. In most instances, these are compatible with rejuvenation procedures in the aesthetic patient. CONCLUSION: The growth of social media is raising awareness of aesthetic procedures and appears to be increasing the number of patients seeking aesthetic treatment. Educating patients about the importance of treating acne vulgaris can improve overall treatment outcomes. In most instances, the presence of acne is not a barrier to aesthetic care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".