Promoting Perioperative Metabolic and Nutritional Care
Bibliographic record
Abstract
AbstractAbstract Surgery represents a major stressor that disrupts homeostasis and can lead to loss of body cell mass. Integrated, multidisciplinary medical strategies, including enhanced recovery programs and perioperative nutrition support, can mitigate the surgically induced metabolic response, promoting optimal patient recovery following major surgery. Clinical therapies should identify those who are poorly nourished before surgery and aim to attenuate catabolism while preserving the processes that promote recovery and immunoprotection after surgery. This review will address the impact of surgery on intermediary metabolism and describe the clinical consequences that ensue. It will also focus on the role of perioperative nutrition, including preoperative nutrition risk, carbohydrate loading, and early initiation of oral feeding (centered on macronutrients) in modulating surgical stress, as well as highlight the contribution of the anesthesiologist to nutritional care. Emerging therapeutic concepts such as preoperative glycemic control and prehabilitation will be discussed. This article is a narrative review that focuses on the role of perioperative nutrition in modulating the surgical stress response, as well as the contribution of the anesthesiologist to nutritional care. Preoperative nutrition risk, carbohydrate loading, early initiation of oral feeding, anesthetic strategies to facilitate nutritional gains, preoperative glycemic control, and prehabilitation will be addressed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".