Efficacy of Fractional 2940-nm Erbium:YAG Laser Combined with Platelet-Rich Plasma Versus its Combination with Low-Level Laser Therapy for Scar Revision.
Bibliographic record
Abstract
Objective: We sought to compare the safety and efficacy of combining fractional 2940-nm Erbium:YAG (Er:YAG) laser with autologous platelet-rich plasma (PRP) versus its combination with low-level laser therapy (LLLT) for enhancing the outcome of postsurgical and post-traumatic scars. Methods: Fourty-five individuals with post-surgical or post-traumatic scars were randomly divided into three groups: Group A received four fractional Er:YAG laser sessions spaced four weeks apart along with eight sessions of intradermal PRP injections spaced two weeks apart; Group B received four fractional Er:YAG laser sessions spaced four weeks apart along with two sessions of light emitting diode (LED) weekly; and Group C received four fractional Er:YAG laser sessions spaced four weeks apart. Treatment efficacy was evaluated using clinical photographs, Vancouver Scar Scale (VSS), patient satisfaction and histopathology. Results: Regarding vascularity following treatment and the total VSS score, there were significant differences between the studied groups, with scoring was the lowest in Group A compared to Group B and C. Patient satisfaction was the highest in Group A compared to Group B and C. Limitations: A limitation of the current study is the short follow-up period. Conclusion: Scar revision therapy using combined fractional Er:YAG laser with either PRP or LLLT were found to be more efficient and superior to fractional Er:YAG laser alone.
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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) | 0.000 | 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) | 0.001 | 0.000 |
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".