Nutrition and the Plastic Surgeon: Possible Interventions and Practice Considerations
Bibliographic record
Abstract
The objective of this article is to convey the importance of nutrition in plastic surgery, to offer possible outpatient nutritional interventions within the surgical care setting, and to guide the plastic surgeon in integrating nutrition as a key practice enhancement strategy for the care of wound patients and beyond. The impact of nutritional status on surgical outcomes is well recognized. Malnutrition is very frequent among the hospitalized patient population and up to 1 in 4 plastic surgery outpatient is at risk for malnutrition. Micro- and macronutrients are both essential for optimal wound healing and although specific patient populations within the field of plastic surgery are more at risk of malnutrition, universal screening, and actions should be implemented. Outpatient interventions to promote adequate nutritional intake and address barriers to the access of fruits and vegetables have included both exposure and incentive interventions. In the clinical setting, universal screening using validated and rapid tools such as the Canadian Nutritional Screening Tool are encouraged. Such screening should be complemented by appropriate blood work, body mass index measurements, and prompt referral to a dietician when appropriate. The notion of prehabilitation has also emerged with impetus in surgery and encompasses the nutritional optimization of patients by promoting the enhancement of functional capacity preoperatively.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".