Consensus Statement on the Prevention and Management of Complications of Fully Ablative Laser Resurfacing of the Face
Bibliographic record
Abstract
OBJECTIVES: To achieve consensus among expert laser surgeons on standards for the prevention and management of adverse events from fully ablative laser resurfacing of the face. MATERIALS AND METHODS: Delphi study with two rounds of ratings and revisions until consensus was achieved. The draft set of statements was developed by a steering committee based on expert clinical experience. This was followed by two rounds of rating and revisions completed by an expert panel, then a virtual consensus meeting. In both rounds, respondents rated the draft statements on a 9-point Likert scale (1 = strongly disagree; 9 = strongly agree) and optionally provided comments. The consensus meeting was supplemented by the results of a systematic review of the literature (from 2000 to 2023). RESULTS: Two rounds of Delphi survey were completed by 34 participants across four countries. Represented specialties were dermatology, facial plastic surgery, plastic surgery, and oculoplastic surgery. The initial 105 statements from round 1 expanded to 112 in round 2, with 96 statements achieving consensus. These included possible adverse events (11 statements); absolute and relative contraindications to treatment (5 statements); preoperative care and antimicrobial prophylaxis precautions (16 statements); intraoperative precautions (17 statements); postoperative care (21 statements); monitoring for and management of infection (16 statements); management of pigmentation changes (6 statements); and management of scarring and incipient scarring (4 statements). CONCLUSION: An international consensus statement was developed for the prevention and management of complications associated with fully ablative laser resurfacing of the face. While expert practices vary, key factors for optimizing outcomes include careful patient selection, counseling, and meticulous pre- and postoperative care. Further research will improve our understanding of this treatment technique.
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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 | 0.181 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".