The use of autologous platelet-rich plasma gel increases wound healing and reduces scar development in split-thickness skin graft donor sites
Bibliographic record
Abstract
The treatment of donor sites after split-thickness skin grafting (STSG) is a routine operation step, and complications at the donor site due to improper operation and care are unwelcome. This study evaluates whether the use of platelet-rich plasma (PRP) applied at the STSG area promotes wound healing and improves scar development. Clinical data of 30 patients who underwent STSG operations between January 2016 and January 2017 for various reasons were retrospectively analyzed. These 30 patients received two treatments and the data were summed up in two groups: the PRP group, which was the study group, included patients who received traditional petrolatum gauze dressing with PRP gel at the donor sites. The petrolatum gauze group, which was the control group, received only petrolatum gauze care without PRP gel. The time and frequency of dressing change were comparable between the two groups, and the mean wound healing times in the PRP group and petrolatum gauze group were 13.89 ± 4.65 and 17.73 ± 5.06 days, respectively, and the difference was statistically significant (p < 0.05). In addition, the total Vancouver scar scale (VSS) scores of the PRP group at 4, 12 and 52 weeks were 6.41 ± 0.77, 4.42 ± 0.43 and 2.41 ± 0.39, respectively, which were statistically significantly lower (p < 0.05) than those of the control group at 7.67 ± 0.64, 6.28 ± 0.62 and 4.29 ± 0.64, respectively. The use of PRP gel can promote wound healing, relieve scar development and alleviate pain at the donor site after STSG.
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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".