A Comprehensive Analysis of Moist Versus Non‐Moist Dressings for Split‐Thickness Skin Graft Donor Sites: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background and Aims: This systematic review and meta-analysis evaluate the efficacy of moist versus non-moist dressings for split-thickness skin graft (STSG) donor sites, focusing on time to healing, pain management, and adverse events to guide clinical practice. Methods: A comprehensive literature search was conducted across databases including Ovid/MEDLINE, Embase, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, and Scopus up to November 28, 2023. The study adhered to PRISMA guidelines. Eligible randomized controlled trials (RCTs) were assessed for quality using the Newcastle-Ottawa Scale and Cochrane risk-of-bias tool, with meta-analysis performed using the DerSimonian and Laird random-effects model. Results: Out of 464 identified studies, 16 RCTs involving 1129 patients were included. Moist dressings such as Tegaderm, Hydrocolloid, Alginate, polyurethane, and hydrofiber showed a faster mean time to healing compared to non-moist dressings like Mepitel and paraffin-impregnated gauze. Hydrocolloid dressings were particularly effective in accelerating wound healing. Additionally, moist dressings were associated with lower pain levels during dressing removal and had comparable rates of adverse events. Conclusion: The evidence strongly supports the use of moist dressings, particularly Hydrocolloid, for STSG donor site coverage. These dressings promote faster healing and superior pain management. The study highlights the need for further research to address existing limitations and refine recommendations for optimal wound care interventions.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.048 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".