Evaluation of Clinical Results, Histological Architecture, and Collagen Expression Following Treatment of Mature Burn Scars With a Fractional Carbon Dioxide Laser
Bibliographic record
Abstract
OBJECTIVE: To assess mature burn scars treated with a fractional carbon dioxide laser for changes in histological architecture, type I to III collagen ratios, density of elastic tissue, and subjective measures of clinical improvements. DESIGN: Uncontrolled, prospective study of patients with mature burn scars, from a clinical and histological perspective. Biopsy specimens were obtained before and 2 months after 3 treatment sessions. The tissue was prepared with Verhoff von Giesen (VVG) stain to discern elastic tissue and Herovici stain to differentiate types I and III collagen. SETTING: Subjects were recruited from the Grossman Burn Centers. PARTICIPANTS: Of 18 patients with mature burn scars, 10 completed the entire treatment protocol. INTERVENTION: Participants received 3 treatments with a fractional carbon dioxide laser. MAIN OUTCOME MEASURES: Vancouver Scar Scale and Patient and Observer Scar Assessment Scale survey scores. In histological analysis, imaging software was used to measure changes in collagen subtype and elastic tissue. A rating scale was developed to assess normal vs scar architecture. RESULTS: The first hypothesis that significant histological improvement would occur and the second hypothesis of a statistically significant increase in type III collagen expression or a decrease in type I collagen expression were confirmed. There were no significant changes in elastic tissue. Statistically significant improvements were seen in all survey data. CONCLUSIONS: Treatment with a fractional carbon dioxide laser improved the appearance of mature burn scars and resulted in a significant improvement in collagen architecture following treatment. Furthermore, in treated skin specimens, a collagen subtype (types I and III collagen) profile resembling that of nonwounded skin was found.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".