Obesity increases the risk of major wound complications following pelvic resection for bone sarcoma
Bibliographic record
Abstract
BACKGROUND: Given the paucity of data, the objective of this study is to evaluate the association between obesity and major wound complications following pelvic bone sarcoma surgery specifically. METHODS: Patients who underwent pelvic resection for bone sarcoma from 2005 to 2021 with a minimum 6-month follow-up were reviewed. Patients with benign tumors, primary soft tissue sarcomas, local recurrence at presentation, pelvic metastatic disease, and underweight patients were excluded. A major wound complication was defined as the need for a secondary debridement procedure. Differences in baseline demographics, surgical factors, postoperative complications, and functional outcomes were compared between obese and nonobese patients. A multivariate logistic regression was performed to identify independent risk factors for major wound complications, and a Kaplan-Meier analysis to estimate overall survival between both groups. RESULTS: ). The obesity group had a significantly higher rate of major wound complication (52% vs. 26%, p = 0.034) and a lower Toronto Extremity Salvage Score at 1-year postoperatively (47.5 vs. 71.4, p = 0.025). Obesity was the only independent risk factor in the multivariate analysis. No differences in overall survival were demonstrated between groups. CONCLUSIONS: Obesity is a significant risk factor for major wound complications in pelvic bone sarcoma treatment. This highlights the importance of careful perioperative optimization and wound management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".