Free Flap Reconstruction in Burns: A Systematic Review of Current Practices and Evidence
Bibliographic record
Abstract
BACKGROUND: The use of microsurgery remains extremely limited in burn management despite offering an alternative in cases where conventional burn reconstruction techniques fall short. This systematic review aims to evaluate the success of microsurgical burn reconstruction in both acute and chronic burn patients and compare it to other surgical modalities as reported in current literature. METHODS: Adhering to PRISMA guidelines, a systematic literature search was conducted across Ovid Medline, Embase, PubMed/Google Scholar databases, spanning publications from 2005 to 2023. Thirteen studies met inclusion criteria. Data were sorted into categories such as study details, patient demographics, burn information, surgical management, and outcomes. RESULTS: The studies encompassed 396 microsurgical reconstructions with a wide age range and varied anatomical regions for both primary and secondary reconstruction. The most common burn etiology was flame, and most acute burn surgeries were performed five to 22 days after injury. The most common acute and nonacute complications were partial necrosis and hematomas, respectively. Findings revealed an average success rate per flap of 92.7% and 95.7% for acute and reconstructive free flaps, respectively. CONCLUSION: Microsurgery offers a promising alternative for complex burn injuries where conventional reconstructive options are exhausted or fall short. However, its success is contingent on patient selection, timing of intervention, and perioperative patient care. The success rate and complication profile of acute microsurgical burn reconstruction are similar to that seen in trauma reconstruction. Secondary microsurgical burn reconstructions have similar success rates to those seen in other elective flaps, such as breast reconstruction. Current usage of microsurgery in burns is low, yet the majority of literature supports expanding its application in the field.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| 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".