Effect of CO₂ fractional laser intervention versus hyaluronidase injection in early scar treatment: a randomized controlled study
Bibliographic record
Abstract
Early intervention for scars is a vital focus in dermatologic surgery, with various treatment options showing potential in enhancing the appearance and texture of scars. This randomized controlled study evaluated the effectiveness of fractional CO₂ laser treatment compared to hyaluronidase injection in managing early scars. Sixty patients with recent scars were randomly assigned to receive either fractional CO₂ laser treatment (n = 30) or hyaluronidase injection (n = 30), with 56 patients completing the study. Treatments were conducted over 4-6 sessions, followed by a 6-month follow-up. The CO₂ laser group showed significantly better results, achieving a 45.3% reduction in scar volume compared to 32.7% in the hyaluronidase group (p < 0.001). Improvements in the Vancouver Scar Scale were also significantly higher in the CO₂ laser group (52.4% ± 15.6% vs. 38.9% ± 14.2%, p < 0.01). Histopathological analysis indicated better collagen organization, improved elastic fiber networks, and lower type I/III collagen ratios in the CO₂ laser group, nearing values typical of normal skin. Both treatments had favorable safety profiles, but the CO₂ laser group needed fewer treatment sessions. These results provide strong evidence that fractional CO₂ laser is a preferred option for early scar management, especially when treatment begins in the third week after scar formation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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 | 0.001 |
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".