Promoting Perioperative Metabolic and Nutritional Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it