Laser Resurfacing Monotherapy for the Treatment of Actinic Keratosis
Bibliographic record
Abstract
Treating actinic keratosis (AK) and photodamaged skin is critical to reduce the risk of progression to skin cancer. Laser resurfacing for AK treatment is available as either lesion-directed or field therapy. Laser resurfacing removes the superficial epidermis and dermis containing actinic damage, promoting re-epithelialization of healthy skin. Although laser resurfacing has been explored as a modality for AK treatment in the literature, studies summarizing its efficacy in the treatment of AK are lacking. This review summarizes existing research on laser resurfacing as a monotherapy for AK treatment, highlighting the various laser resurfacing modalities available for AK treatment as well as their complications and efficacy in comparison to other therapies. Despite longer healing time, fully ablative laser resurfacing, including carbon dioxide and erbium-doped yttrium aluminum garnet were found to be more effective for AK treatment than fractional ablative techniques. Although some studies suggest laser resurfacing monotherapy as less efficacious than photodynamic therapy, and equally effective to 5-fluorouracil and 30% trichloroacetic acid, clinical trials of larger sample size are required to establish stronger evidence-based conclusions. Moreover, laser resurfacing used as lesion-directed therapy, as opposed to the usual field-therapy, requires further investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 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".