The Effect of Negative Pressure Dressing on Skin Graft Donor Wound Scarring
Bibliographic record
Abstract
Background: Negative pressure wound therapy (NPWT) reduces bacterial load, creates a vacuum, and promotes tissue development. The Vancouver Scar Scale (VSS) standardizes scar severity assessment after split-thickness skin transplants. Vascularity, height/thickness, pliability, and pigmentation are used to quantify scar quality for healthcare practitioners. Objectives: The objective of this study is to compare the effect of using the NPWT versus standard moist dressing to assess scaring of skin donor site wound. Patients and methods: This prospective study was conducted upon patients who previously underwent negative pressure wound therapy as a dressing method to treat the donor site wound following split-thickness skin graft (STSG) procedures were reviewed and assessed for the wound scarring quality using VSS after 3 months post-operatively. Results: There were 12 skin graft donor sites wounds included in this study that could be followed-up and assessed for scar quality after 3 months postoperatively. The study group had a mean age of 35.33 years (SD=16.08), comprising 4 males (66.67%) and 2 females (33.33%). Comorbidities included diabetes in 2 patients (33.33%) and hypertension in 1 patient (16.67%). Wounds resulted from burns in 4 patients (66.67%), trauma in 1 patient (16.67%), and scars in 1 patient (16.67%). NPWT resulted in significantly better wound scarring quality (Vancouver Scar Scale: 1.5=0.76) compared to standard dressing (4.83=1.34) (P=0.0004). Conclusion: In our study, we conclude the NPWT enhances wound scarring quality, reduces the scar severity and potentially improves the patient outcomes and satisfactions, when used following split-thickness skin graft procedure particularly beneficial in burns and traumatic injuries.
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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 | 0.002 |
| 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) | 0.002 | 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".