Treating pain on skin graft donor sites: Review and clinical recommendations
Bibliographic record
Abstract
Split-thickness skin grafting is the most common reconstructive procedure in managing burn injuries. Harvesting split-thickness skin creates a new partial thickness wound referred to as the donor site. Pain at the donor site is reported to be one of the most distressing symptoms during the early postoperative period. Here, we (a) identify strategies for managing donor site pain, (b) assess the quality of individual studies, and (c) formulate evidence-based recommendations based on the amount and consistency of evidence. Our analysis revealed five distinct approaches to minimize donor site pain. These include: continuous subcutaneous local anesthetic infusion (three studies), subcutaneous anesthetic injection (five studies), topical agents (six studies), nonpharmacological interventions (three studies), and wound dressings (18 studies). Available randomized control trials typically evaluated pain on standardized scales (i.e. Visual Analog Scale, Numerical Rating Scale), and compared the experimental group with standard care. Recommended treatments include: (a) subcutaneous anesthetic injection of adrenaline-lidocaine; (b) ice application; (c) topical agents, such as lidocaine and bupivacaine; and (d) hydrocolloid- and polyurethane-based wound dressings accompanied with fibrin sealant. Methodologically sound randomized control trials examining the efficacy of modified tumescent solution, ropivacaine, plasma therapy, noncontact ultrasound, and morphine gels are lacking and should be a priority for future research.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".