Clinical and Histological Assessment of Combined Fractional CO<sub>2</sub> Laser and Growth Factors Versus Fractional CO<sub>2</sub> Laser Alone in the Treatment of Facial Mature Burn Scars: A Pilot Split‐Face Study
Bibliographic record
Abstract
Background and Objectives To investigate the therapeutic efficacy and safety of growth factors combined with fractional carbon dioxide (CO2) laser in comparison with fractional CO2 alone in a sample of patients with facial mature burn scars. Study Design/Materials and Methods Fifteen Egyptian patients with bilateral facial burn scars were treated with six sessions of fractional CO2 laser at 6‐week intervals. Following each laser session, a topical growth factors cocktail was applied to one side of the face in a split‐face manner. Clinical evaluation by Vancouver Scar Scale (VSS), Patient and Observer Scar Assessment Scale (PSOS), and photography before and 2 months after the last laser session was done. Three millimeter punch biopsies were obtained from each side of the face pre‐ and 1‐month posttreatment to measure the mean area percent of collagen. Results Posttreatment, both VSS and PSOS scores decreased on both sides of the face being more significant on the growth factors treated side, showing more scar pliability and shorter downtime (P = 0.001). A significant difference in the mean area percent of collagen was also noted on both sides. Conclusion Adding topical growth factors to fractional CO2 laser treatments is effective and safe with better results as regards scar pliability and shorter downtime than fractional CO2 laser alone. Lasers Surg. Med. © 2020 Wiley Periodicals, Inc.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".