Treatment of Surgical Scars with Fractional Carbon Dioxide (CO <sub>2</sub> ) Laser: A Randomized Controlled Trial
Bibliographic record
Abstract
Object: The aim of this study was to compare the long-term effects of fractional carbon dioxide (CO 2 ) laser treatment with traditional therapy on surgical scars by analyzing and comparing observational indicators. Approach: A randomized controlled trial was conducted on 116 patients who received scar treatment in our hospital, of which 58 patients received fractional CO 2 laser treatment, and 58 patients received injection treatment. The outcome measures comprised the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS). The observation intervals occur at specific times. Evaluated at 6 months of follow-up, the outcome was blinded, on-site evaluation using the dermatological appearance scale (DAS) and visual analog scale (VAS). Results: Patients in the fractional carbon-dioxide laser (CO 2 FL) group rated better than those in the injection group in the POSAS, VSS, DAS, and VAS scores ( p < 0.05). The therapeutic efficacy and patients’ satisfaction of the research group were superior to those in the control group. Innovation: Scars following surgical procedures can be treated in a variety of methods, but there is no consensus on the best method. CO 2 FL has exhibited safety and is more effective than traditional injection treatments for surgical scars. It is a worthwhile approach to consider in clinical treatment. Conclusions: This study demonstrates that CO 2 FL achieves more significant long-term results in surgical scars, including improved scar appearance, safety, and patient satisfaction.
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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 | 0.004 | 0.004 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".