Morbid Obesity Increases the Risk of Postoperative Wound Complications, Infection, and Repeat Surgical Procedures Following Upper Extremity Limb Salvage Surgery for Soft Tissue Sarcoma
Bibliographic record
Abstract
Background: Obesity is a known risk factor for wound complications; however, unlike elective upper extremity procedures, where obesity can be modified preoperatively, excision of soft tissue sarcomas (STSs) is not elective, and as such, obesity cannot be modified. There is a paucity of data concerning the impact of obesity on wound healing in upper extremity sarcoma surgery. Methods: A total of 261 (159 males and 102 females) patients with a STS of the upper extremity from 2006-2014 were reviewed. The mean age and body mass index (BMI) were 56 (18-97) years and 26.6 (15.4-40.8) kg/m 2 , respectively. Sixty-nine patients (26%) were classified as obese (BMI ⩾30 kg/m 2 ): class I (obese, BMI = 30-34.9 kg/m 2 ; n = 48, 18%), class II (severely obese, BMI = 35.0-39.9 kg/m 2 ; n = 16, 6%), and class III (morbidly obese, BMI ≥ 40 kg/m 2 ; n = 5, 2%). Functional outcomes were also compared between obese and nonobese patients using the Musculoskeletal Tumor Society (MSTS) 1993 rating system and Toronto Extremity Salvage Scores (TESS). Results: Forty-nine patients (19%) sustained a wound dehiscence, delayed healing, or infection. Class III obesity increased the risk of wound complications (hazard ratio [HR] = 8.19, 95% confidence interval [CI] = 1.96-22.96, P < .001) and infection (HR = 10.09, 95% CI = 1.60-34.83, P = .01). There was no difference in the mean TESS (93 vs 90, P = .13) or MSTS93 (95 vs 93, P = .39) between obese and nonobese patients. Conclusions: The results of this study indicate morbid obesity significantly increased the risk of a postoperative wound complication and infection. However, following upper extremity limb salvage surgery, obese patients should expect to have excellent functional outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".