Risk factors for postoperative wound complications after extremity soft tissue sarcoma resection: A systematic review and meta-analyses
Bibliographic record
Abstract
INTRODUCTION: Advancements in imaging, surgical, and radiation techniques have made resection of larger and more extensive extremity soft tissue sarcomas (ESTS) possible but with the potential for high complication rates. This study summarizes complication and reoperation rates associated with resection of ESTS and reviews predictors for wound complications. METHODS: A systematic review of the literature on ESTS in adults was undertaken from the four databases MEDLINE, Embase, MEDLINE In-Process & Other Non-Indexed Citations, and the Cochrane Central Register of Controlled Trials (CCRCT). Meta-analyses of the complications, reoperations, and risk factors were performed. RESULTS: In the twenty-one studies included, there was an overall wound complication rate of 30.2% (95% CI 26.56-33.47) and a reoperation rate of 13.37% (95% CI 10.21-16.52) in 5628 patients. Individual studies reported that older patient age, obesity, smoking, diabetes, large tumor size, tumor site, and preoperative radiotherapy were associated with adverse outcomes. Tumors of the lower limb, diabetes, smoking, obesity, and radiation were identified as independent predictors of wound complications in meta-analysis. A high level of heterogeneity between studies limited pooled analysis for many variables. CONCLUSIONS: Despite advancements in the treatment of ESTS, postoperative complication rates remain high. Awareness of the risk factors for wound complications, especially those that may be modifiable, is essential to decrease postoperative morbidities in these patients to improve treatment outcomes and quality of life.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".