Periprocedural Skincare for Nonenergy and Nonablative Energy‐Based Aesthetic Procedures in Patients With Skin of Color
Bibliographic record
Abstract
BACKGROUND: Anti-aging facial procedures with nonenergy and nonablative energy devices are increasingly popular among patients with skin of color (SOC). Algorithms have addressed the measures to reduce the side effects related to aesthetic procedures, but few focus on SOC patients and periprocedural integrating skincare. METHODS: Eight dermatologists from Brazil, Canada, South Africa, Mexico, and the USA participated in a meeting and an online follow-up to develop an algorithm for periprocedural skincare for nonenergy and nonablative energy-based facial aesthetic procedures in patients with SOC. A Delphi method was used to develop this algorithm and integrate information from the literature with panels' clinical experience and opinion, resulting in the current algorithm. RESULTS: The algorithm has five sections, starting with a medical history and skin examination, followed by pretreatment measures beginning 2-4 weeks before the procedure, then measures on the day of the procedure, aftercare 1-7 days after the procedure, and follow-up care 1-4 weeks after the procedure and ongoing. CONCLUSIONS: This algorithm provides guidelines for treatment optimization of non-energy, non-ablative energy-based devices for SOC patients. It also provides physicians with skincare recommendations pre-, peri-, and post-aesthetic procedures.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".