Preoperative nutrition care in Enhanced Recovery After Surgery programs: are we missing an opportunity?
Bibliographic record
Abstract
PURPOSE OF REVIEW: A key component of Enhanced Recovery After Surgery (ERAS) is the integration of nutrition care elements into the surgical pathway, recognizing that preoperative nutrition status affects outcomes of surgery and must be optimized for recovery. We reviewed the preoperative nutrition care recommendations included in ERAS Society guidelines for adults undergoing major surgery and their implementation. RECENT FINDINGS: All ERAS Society guidelines reviewed recommend preoperative patient education to describe the procedures and expectations of surgery; however, only one guideline specifies inclusion of routine nutrition education before surgery. All guidelines included a recommendation for at least one of the following nutrition care elements: nutrition risk screening, nutrition assessment, and nutrition intervention. However, the impact of preoperative nutrition care could not be evaluated because it was rarely reported in recent literature for most surgical disciplines. A small number of studies reported on the preoperative nutrition care elements within their ERAS programs and found a positive impact of ERAS implementation on nutrition care practices, including increased rates of nutrition risk screening. SUMMARY: There is an opportunity to improve the reporting of preoperative nutrition care elements within ERAS programs, which will enhance our understanding of how nutrition care elements influence patient outcomes and experiences.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".