Efficacy of Nd:YAG Laser and Intralesional Triamcinolone Injection Combination Therapy in the Postoperative Management of Keloids
Bibliographic record
Abstract
BACKGROUND: Keloids, characterized by protruding scars that extend beyond the original skin damage site, cause significant emotional stress and reduced quality of life. Their exact pathogenesis remains unclear, with various hypotheses including growth factor imbalances and extracellular matrix changes. No single treatment is universally accepted, but multiple modalities like triamcinolone acetonide injection (TAC), laser therapies, and surgery are commonly used. METHODS: This retrospective study involved East Asian patients who underwent keloid scar excision between March 2019 and June 2022. Patients were divided into two groups: one receiving only TAC injections and the other a combination of TAC and Nd:YAG laser therapy. The efficacy of treatments was evaluated using the modified Vancouver Scar Scale (mVSS) and the Patient and Observer Scar Assessment Scale (POSAS), with follow-ups at six and twelve months after operation. RESULTS: The study involved 111 patients. Both treatment groups showed significant improvements in mVSS and POSAS scores, but the combination therapy group demonstrated a statistically significant improvement in POSAS scores and lower recurrence rates at 12 months compared to the TAC-only group. However, there was no significant difference in patient satisfaction between the groups. CONCLUSION: Dual therapy involving TAC injection and Nd:YAG laser treatment was more effective than TAC injection alone for managing keloid scars after surgery. This combination therapy showed better outcomes in preventing keloid recurrence and improving scar status at 12 months after operation, along with significant improvements in patient-reported outcomes. LEVEL OF EVIDENCE II: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".