Clinical efficacy of utilizing Ultrapulse <scp>CO</scp><sub>2</sub> combined with fractional <scp>CO</scp><sub>2</sub> laser for the treatment of hypertrophic scars in Asians—A prospective clinical evaluation
Bibliographic record
Abstract
Summary Background and Objective Hypertrophic scarring is seen regularly. Tissue penetration of laser energy into hypertrophic scars using computer defaults from some lasers may be insufficient and penetration not enough. We have developed a treatment with an interrupted laser “drilling” by the Ultrapulse CO2 (Manual Fractional Technology, MFT) and, a second pass, with fractional CO2. The MFT with fractional CO2 lasers to treat hypertrophic scars is evaluated. Study Design/Materials and Methods A total of 158 patients with hypertrophic scars had three sessions of MFT with fractional CO2 laser at 3‐month intervals. Evaluations made before and 6 months after the 3rd treatment: (1) the Vancouver Scar Scale (VSS), (2) the University of North Carolina (UNC) Scar Scale, and (3) a survey of patient satisfaction. Results All data were analyzed using a t‐test before and after treatment. The VSS score decreased from 9.35 to 3.12 (P<.0001), and the UNC Scar Scale score decreased from 8.03 to 1.62 (P<.0001). The overall satisfaction rate was 92%. No long‐term complications occurred in the clinical trial. Conclusion The interrupted laser drilling by MFT and a fractional CO2 laser had profound effects on the hypertrophic scars treated. It works by increasing the penetration depth of the CO2 laser in the scar tissue, exerting more precise effects on the hypertrophic scars. MFT combined with fractional CO2 laser has the potential to be a major advance in the treatment of hypertrophic scars.
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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 | 0.001 |
| 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 | 0.001 | 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".