Systematic Review of Skin Graft Donor-Site Dressings
Bibliographic record
Abstract
BACKGROUND: Debate continues about what split-thickness skin graft donor-site dressing provides the best outcomes for patients at the lowest cost. The goal of this systematic review was to determine which donor-site dressings are associated with the best outcomes for the following: pain, infection rate, healing quality, healing rate, quality of life, and cost. METHODS: A comprehensive literature review and assessment was undertaken by two independent reviewers. Articles were selected using specific inclusion criteria. Split-thickness skin graft donor-site dressings were classified as either moist or nonmoist based on the state of the dressing upon initial application. Methodological quality of randomized controlled trials was assessed using the Jadad scale. RESULTS: Seventy-five relevant articles were included in the final analysis, three of which were review articles. The most commonly measured outcome was healing rate (64 of 72), followed by pain (58 of 72), infection rate (40 of 72), healing quality (40 of 72), and cost (15 of 72). No studies measured quality of life. The majority of articles were randomized controlled trials (35 of 75), followed by observational studies (22 of 75), unsystematic clinical observations (15 of 75), and review articles (three of 75). It was difficult to compare moist and nonmoist dressings in this review because of the methodological heterogeneity of the included articles. The available evidence suggests, however, that moist dressings are superior in terms of pain. CONCLUSIONS: Some weak evidence exists that supports "wet dressings." To determine the best split-thickness skin graft donor-site dressing, more methodologically sound randomized controlled trials are needed. Trials with parallel economic evaluations should be undertaken to answer this question.
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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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".