The Role of Negative Pressure Wound Therapy in Temporizing Traumatic Wounds before Lower Limb Soft Tissue Reconstruction: A Systematic Review
Bibliographic record
Abstract
Background: In practice, there is often a delay from initial debridement and temporary fixation to definitive soft tissue coverage of traumatic leg wounds. Without clear evidence, conservative negative pressure wound therapy (NPWT) is increasingly used to temporize these wounds. This systematic review summarizes and synthesizes the literature on using NPWT to temporize traumatic leg wounds before surgery in adult surgical patients. Methods: A comprehensive search of Medline, Embase, and Cochrane Library was performed from inception until July 2022, inclusively. Two independent reviewers performed screening, data extraction, and risk of bias assessment. Primary English studies, including adult patients (≥16 years old) with a fracture below the knee up to and including the ankle that received NPWT to temporize wounds before definitive soft tissue reconstruction with a flap and/or graft, were included. Results: Thirty-four studies, including 804 patients who received NPWT, were included. The partial/total flap loss rate was 6.95% (n = 9 studies), the infection rate was 19.5% (n = 25 studies), the nonunion rate was 18% (n = 15 studies), the delayed union rate was 9.31% (n = 3 studies), and the amputation rate was 15.4% (n = 6 studies). The mean late stay was 43.1 days (n = 9 studies), and the follow-up length was 23.7 months (n = 9 studies). The mean time to wound healing was 5.63 months (n = 2 studies), and the time to soft tissue coverage was 40.8 days (n = 12 studies). The mean time to achieve bone union was 7.26 months (n = 6 studies). Conclusion: NPWT can be used to temporize traumatic wounds while awaiting soft tissue reconstruction.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".