Fractionated Laser Skin Resurfacing Treatment Complications: A Review
Bibliographic record
Abstract
BACKGROUND: Fractional photothermolysis represents a new modality of laser skin resurfacing that was developed to provide a successful clinical response while minimizing postoperative recovery and limiting treatment complications. OBJECTIVES: To review all of the reported complications that develop as a result of fractional ablative and nonablative laser skin resurfacing. METHODS: A literature review was based on a MEDLINE search (1998-2009) for English-language articles related to laser treatment complications and fractional skin resurfacing. Articles presenting the highest level of evidence and the most recent reports were preferentially selected. RESULTS: Complications with fractional laser skin resurfacing represent a full spectrum of severity and can be longlasting. In general, a greater likelihood of developing post-treatment complications is seen in sensitive cutaneous areas and in patients with intrinsically darker skin phototypes or predisposing medical risk factors. CONCLUSIONS: Although the overall rate of complications associated with fractional laser skin resurfacing is much lower than with traditional ablative techniques, recent reports suggest that serious complications can develop. An appreciation of all of the complications associated with fractional laser skin resurfacing is important, especially given that many of them can be potentially prevented. The authors have indicated no significant interest with commercial supporters.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".