Nutrition Therapy for the Critically Ill Surgical Patient With Aortic Aneurysmal Rupture
Bibliographic record
Abstract
BACKGROUND: Our goal is to define nutrition therapy in critically ill patients after surgical repair of acute ruptured or dissecting aortic aneurysm to identify opportunities for quality improvement. METHODS: International, prospective studies in 2007-2009 and 2011 were combined. Sites provided institutional and patient characteristics including from intensive care units (ICUs) admission to ICU discharge for a maximum of 12 days. We selected patients with aortic aneurysmal rupture or acute dissection staying in the ICU for ≥ 3 days. RESULTS: There were 104 eligible patients from 72 distinct ICUs analyzed. Overall, 86.5% received artificial nutrition. There were 50.0% patients who received enteral nutrition (EN) only, 29.8% patients received a combination of EN and parenteral nutrition (PN), 6.7% patients received PN only, and 13.5% did not receive any nutrition. The mean time from admission to initiation of EN was 3.0 days (SD ± 2.4 days). The adequacy of calories from nutrition support was 46.8% (range 0%-111%) with a mean of 10.0 kcal/kg/day. Of the total of 83 patients who received EN, 53 patients (63.8%) had interruption of EN. The reasons included fasting, intolerance, patients deemed too sick for enteral feeding, and loss of enteral feeding route. For patients with gastrointestinal intolerance, 3/30 patients (10%) received small bowel feeding and 23/30 patients (76.7%) of patients received motility agents. CONCLUSION: Postoperative critically ill patients with aortic aneurysmal rupture or acute dissection are at high risk for inadequate nutrition therapy, and there may be inadequate utilization of strategies to improve nutrition uptake.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".