Efficacy of Long-Pulsed 1064 nm Nd:YAG Laser for Hypertrophic Scars: A randomized controlled trial
Bibliographic record
Abstract
Background: In the industrialized world, a hypertrophic scar is a major health risk. Purpose: Investigating the impact of a 532 nm wavelength Nd-YAG laser on hypertrophic scar was the aim of this study. Materials and methods: 40 patients with hypertrophic scars, both male and female, between the ages of 18 and 40, were selected from the burn patients' clinic at Cairo University's Faculty of Physical Therapy. They were divided randomly into two equal groups. During the course of treatment, 20 patients in Group A (control group) received regular medical care, nursing, physical therapy, and a sham laser. On the other hand, 20 patients in Group B (the study group) received 532 nm laser radiation in addition to regular medical care, nursing, and physical therapy. 14 sessions of treatment were held every two weeks. Among the outcome measures were the Vancouver Scar Scale score grading system and scar volume measurements for every subject. The evaluation methods were used prior to the start of treatment (Pre), three months later (Post1), and seven months later (Post 2) after the start of treatment. Results: Significant differences were observed in scar volume and Vancouver Scar Scale scores before, after, and after treatment in the two groups (control and study) with greater improvement in study group than control group. Conclusion: the study's findings indicate that hypertrophic scar healing is accelerated with Nd-YAG laser.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".