Implementing the PEP uP Protocol in Critical Care Units in Canada
Bibliographic record
Abstract
BACKGROUND: Previous studies have documented widespread iatrogenic underfeeding in intensive care unit (ICU) patients. In an experimental setting, we demonstrated the safety and efficacy of a novel enteral feeding protocol designed to overcome the main barriers to adequate delivery of enteral nutrition (EN), the Enhanced Protein-Energy Provision via the Enteral Route Feeding Protocol (PEP uP protocol). The purpose of this article is to describe our experience with implementing this feeding protocol under "real-world" settings in Canada. MATERIALS AND METHODS: This study is a multicenter quality improvement initiative with a concurrent control group. Selected ICUs implemented the PEP uP protocol, and nutrition practices and outcomes were compared with a concurrent control group of ICUs. RESULTS: In 2013, of the 24 ICUs from Canada that participated in the International Nutrition Survey, 8 implemented the PEP uP protocol and the remaining 16 served as concurrent control sites. Patients at PEP uP sites received 60.1% of their prescribed energy requirements from EN compared with 49.9% in patients from control hospitals (P = .02). In addition, patients in PEP uP protocol sites received more protein from EN (61.0% vs 49.7% of prescribed amounts; P = .01), were more likely to receive protein supplements (71.8% vs 47.7%; P = .01), and were more likely to receive >80% of their protein requirements by day 3 (46.1% vs 29.3%; P = .05) compared with patients in control hospitals. CONCLUSIONS: In the real-life setting, the PEP uP protocol can improve the delivery of EN to critically ill patients.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".