Fractional resurfacing in the Asian patient: Current state of the art
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Fractionated photothermolysis (FP) has revolutionized modern laser technology. By creating selective columns of microthermal damage, fractionated devices allows for greater treatment depths to be achieved without the prolonged downtime and risk of complications seen in traditional fully ablative laser resurfacing. Fractional resurfacing is a proven method to treat a variety of cutaneous conditions. In the Caucasian patient, a wide range of devices and treatment settings can be utilized safely and effectively. However, ethnic skin requires special consideration due to its unique pigmentary characteristics and clinical presentations. In this review article, we detail the current indications and strategies to optimize results and mitigate complications when utilizing fractional resurfacing for the Asian patient. METHODS: ) laser, erbium yttrium aluminum garnet and yttrium scandium gallium garnet (YSGG) laser. These data were integrated with the expert opinion of the authors. CONCLUSION: Taking into account the unique characteristics and cosmetic concerns of the Asian population, fractional resurfacing can be considered a safe and effective option for the treatment of atrophic and hypertrophic scarring, and photorejuvenation in ethnic skin types. Select cases of melasma may be treated with fractionated non-ablative devices, but utilized with caution. The predominant complication associated with fractional resurfacing for these conditions is post-inflammatory hyperpigmentation (PIH) and rebound worsening of melasma. A greater number of treatments at lower density settings and wider treatment intervals typically produce the lowest risks of PIH without compromising treatment efficacy. Lasers Surg. Med. 49:45-59, 2017. © 2016 Wiley Periodicals, Inc.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".