Implementation of a best‐practice guideline: Early enteral nutrition in a neuroscience intensive care unit
Bibliographic record
Abstract
Current evidence suggests that early enteral nutrition is a best practice and leads to improved clinical outcomes. An evidence-based practice project was implemented in a busy neurointensive care unit in a midwestern tertiary care facility that was designed to improve care by implementing the early nutrition portion of Guidelines for the Provision and Assessment of Nutrition Support Therapy in the Adult Critically Ill Patient: Society of Critical Care Medicine and the American Society for Enteral and Parenteral Nutrition. The Registered Nurses' Association of Ontario's (RNAO) Toolkit: Implementation of Best Practice Guidelines (BPGs) was selected and followed to guide implementation and achieve optimal results. During a 90-day implementation period, this project resulted in a 100% improvement in early nutrition. Interventions included the use of a series of cards that reminded the team to order enteral nutrition and prepacked bundles of nasogastric tube supplies. The RNAO toolkit served as a structured and effective step-by-step methodology for the implementation of a BPG.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".