Local and Systemic Treatments for Acute Edema After Burn Injury: A Systematic Review of the Literature
Bibliographic record
Abstract
Burn injury is a complex trauma that results in local and generalized edema. Edema fluid limits the exchange of vital nutrients in healing the burn wound and will compromise vulnerable tissues. Although the importance of edema control in tissue salvage is recognized, treatments targeted at edema control have not been critically reviewed. Thus, the objective was to assess the evidence for the effectiveness of local and systemic treatments for edema management immediately after burn injury. Searches for randomized controlled trials were conducted of online databases, research and thesis registers, and grey literature repositories. Handsearches included journals, bibliographies, and proceedings. Authors were contacted to clarify and submit extra study details. Eight studies were included. Management of acute major burn resuscitation including colloid increases lung edema (mean difference [MD], 0.04 ml/ml alv vol; 95% confidence interval [CI], 0.03-0.04; P < .00001) and mortality (risk ratio, 3.67; 95% CI, 1.16-11.58; P = .03). Continuous administration of vitamin C in acute burn resuscitation reduces local wound edema (MD, -3.50 ml/g; 95% CI, -4.63 to -2.37; P < .00001) and systemic fluid retention (MD, -8.60 kg; 95% CI, -13.47 to -3.73; P = .0005). Local acute hand burn edema is reduced (MD, -29.00 ml; 95% CI, -53.14 to -4.86; P = .02), and active hand motion increased (MD, 10.00°; 95% CI, 4.58-15.42; P = .0003), using electrical stimulation with usual physiotherapy. Each review outcome was based on a small single-facility study. Thus, future research in intervention for acute burn edema must focus on multicentre trials and validation of outcome measures in the burn population.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".