Outcomes of Free Muscle Flaps versus Free Fasciocutaneous Flaps for Lower Limb Reconstruction following Trauma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Free flap reconstruction of the lower limb following trauma often suffers higher complication rates than other areas of the body. The choice of muscle or fasciocutaneous free flap is an area of active debate. METHODS: A systematic review of EMBASE, MEDLINE, PubMed, and Cochrane Register from inception to April 1, 2022 was performed. Articles were assessed using the methodological index for non-randomized studies instrument. The primary outcome was to assess and compare the major surgical outcomes of partial or total flap failure, reoperation, and amputation rates. RESULTS: Seventeen studies were included. All studies were retrospective in nature, of level three evidence, and published between 1986 and 2021. The most common muscle and fasciocutaneous free flaps used were latissimus dorsi flap (38.1%) and anterolateral thigh (ALT) flap (64.8%), respectively. Meta-analysis found no significance difference in rates of total flap failure, takeback operations, or limb salvage, whereas partial flap failure rate was significantly lower for fasciocutaneous flaps. The majority of studies found no significant difference in complication rates, osteomyelitis, time to fracture union, or time to functional recovery. Most, 82.4% (14/17), of the included studies were of high methodological quality. CONCLUSION: The rate of total flap failure, reoperation, or limb salvage is not significantly different between muscle and fasciocutaneous free flaps after lower limb reconstruction following trauma. Partial flap failure rates appear to be lower with fasciocutaneous free flaps. Outcomes traditionally thought to be managed better with muscle free flaps, such as osteomyelitis and rates of fracture union, were comparable.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".