A Comprehensive Analysis of Moist Versus Non‐Moist Dressings for Split‐Thickness Skin Graft Donor Sites: A Systematic Review and Meta‐Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it