The Impact of Protein Nutritional Supplementation for Massive Weight Loss Patients Undergoing Abdominoplasty
Bibliographic record
Abstract
BACKGROUND: As more patients undergo bariatric surgery to assist with weight loss, the demand for post-bariatric body contouring surgery, to address both functional and aesthetic concerns, is increasing. However, high wound healing complication rates remain a significant problem for these patients. One theory is that chronic malnourishment and hypoproteinemia may contribute significantly to these wound healing complications. OBJECTIVE: The purpose of this study was to determine the effect of pretreatment protein nutritional supplementation on wound healing in post-bariatric surgery massive weight loss patients undergoing abdominoplasty. Our hypothesis was that protein supplementation would decrease wound healing complications. METHODS: A retrospective review was performed of 23 post-bariatric surgery patients undergoing abdominoplasty who received pretreatment protein nutritional supplementation. This group was compared with a historical control group of 23 post-bariatric surgery patients who underwent abdominoplasty in the period immediately before the implementation of the protein supplementation protocol. Patient demographics and procedural characteristics were similar for the two groups. RESULTS: Forty-six patients were identified who had undergone abdominoplasty, half of whom were prescribed the protein supplementation protocol. Overall wound healing complication rates were significantly lower in the protein-supplemented group (0.0% vs. 21.8%, p = 0.04). There was no significant difference between the protein supplementation and historical control groups in regards to total complication rate. CONCLUSIONS: Pretreatment protein supplementation is a simple intervention that can significantly decrease wound healing complications in post-bariatric surgery massive weight loss patients undergoing abdominoplasty. LEVEL OF EVIDENCE 4: Therapeutic.
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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) | 0.001 | 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".