Nutrition Therapy for the Critically Ill Surgical Patient
Bibliographic record
Abstract
BACKGROUND: To identify opportunities for quality improvement, the nutrition adequacy of critically ill surgical patients, in contrast to medical patients, is described. METHODS: International, prospective, and observational studies conducted in 2007 and 2008 in 269 intensive care units (ICUs) were combined for purposes of this analysis. Sites provided institutional and patient characteristics and nutrition data from ICU admission to ICU discharge for maximum of 12 days. Medical and surgical patients staying in ICU at least 3 days were compared. RESULTS: A total of 5497 mechanically ventilated adult patients were enrolled; 37.7% had surgical ICU admission diagnosis. Surgical patients were less likely to receive enteral nutrition (EN) (54.6% vs 77.8%) and more likely to receive parenteral nutrition (PN) (13.9% vs 4.4%) (P < .0001). Among patients initiating EN in ICU, surgical patients started EN 21.0 hours later on average (57.8 vs 36.8 hours, P < .0001). Consequently, surgical patients received less of their prescribed calories from EN (33.4% vs 49.6%, P < .0001) or from all nutrition sources (45.8% vs 56.1%, P < .0001). These differences remained after adjustment for patient and site characteristics. Patients undergoing cardiovascular and gastrointestinal surgery were more likely to use PN, were less likely to use EN, started EN later, and had lower total nutrition and EN adequacy rates compared with other surgical patients. Use of feeding and/or glycemic control protocols was associated with increased nutrition adequacy. CONCLUSIONS: Surgical patients receive less nutrition than medical patients. Cardiovascular and gastrointestinal surgery patients are at highest risk of iatrogenic malnutrition. Strategies to improve nutrition performance, including use of protocols, are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".