Bridging the Evidence–Practice Gap in Hyperglycemia Management During Enteral Nutrition: A Multidimensional Implementation Study in Critically Ill Patients
Bibliographic record
Abstract
Long-term hyperglycemia poses an elevated risk of infection, organ dysfunction, and increased mortality. Despite the availability of strategies for managing hyperglycemia, implementation effectiveness remains suboptimal. Therefore, this study investigates the factors hindering successful implementation and identifies potential change strategies. This study established evidence-based questions and formulated quality indicators. A baseline survey assessed existing practices. Subsequently, obstacle factors were analyzed using the Ottawa Model of Research Use, considering three dimensions: innovation, potential adopters, and practice environment; change strategies were then developed. The study revealed a significant gap between evidence and practice in hyperglycemia management during enteral nutrition in critically ill patients; adherence to 20 of 25 quality indicators was below 60%. Key obstacle factors included inadequate medical staff knowledge, a lack of standardized management processes, and the complexity of the evidence base. To address these challenges, the research team proposed intervention strategies including strengthening standardized hyperglycemia management protocols, establishing multidisciplinary teams, and providing systematic training. This study offers a framework for improving the quality of hyperglycemia management during enteral nutrition in critically ill patients and promoting the translation of evidence-based practice. Trial Registration: Fudan University Center for Evidence-Based Nursing: ER20240865.
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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 | 0.072 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".