Nonablative Fractional Diode Laser Resurfacing (1440 nm and 1927 nm) for Photoaged Skin
Bibliographic record
Abstract
BACKGROUND: Nonablative lasers treat photoaged skin and stimulate new collagen formation while sparing epidermal damage. OBJECTIVE: To evaluate the effectiveness and safety of nonablative fractional diode combination laser skin resurfacing treatment (1440 and 1927 nm) in mild-to-moderate photoaged skin. MATERIALS AND METHODS: The entire face was treated with both 1440-nm and 1927-nm wavelengths per treatment, with a total of 4 treatments spaced 1 month apart. Follow-up occurred at 1 and 3 months post-treatment. Outcomes were improvement in the appearance of ≥1 measure of photodamage (rhytides, skin texture, dyschromia/pigment, skin radiance, pore size, and overall appearance) at the 3-month (primary) and 1-month (secondary) follow-up visits. Safety was monitored throughout the study. RESULTS: Participants ( N = 28; 89% female; mean age, 40 years) experienced significant mean improvement from baseline in all measures of photodamage with combination laser treatment at 1 and 3 months post-treatment (all p < .001). No serious adverse events occurred. Post-treatment erythema and edema were minimal, and pain levels remained consistent throughout treatment. Most participants (96.4%) considered their overall appearance as improved and expressed satisfaction with treatment outcomes. CONCLUSION: Nonablative combination laser skin resurfacing treatment was well tolerated and significantly improved measures of photodamage in photoaged skin across diverse skin types.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".