Bridging the Guideline–Practice Gap in Critical Care Nutrition
Bibliographic record
Abstract
Several clinical practice guidelines focusing on nutrition therapy in mechanically ventilated, critically ill patients are available to assist busy critical care practitioners in making decisions regarding feeding their patients. However, large gaps have been observed between guideline recommendations and actual practice. To be effective in optimizing nutrition practice, guideline development must be followed by systematic guideline implementation strategies. Systematic reviews of studies evaluating guideline implementation interventions outside the critical care setting found that these strategies, such as reminders, educational outreach, and audit and feedback, produce modest to moderate improvements in processes of care, with considerable variation observed both within and across studies. Unfortunately, the optimal strategies to implement guidelines in the intensive care unit are poorly understood, with scarce data available to guide our decisions on which strategies to use. The authors identified 3 cluster randomized trials evaluating the implementation of nutrition guidelines in the critical care setting. These studies demonstrated small improvements in nutrition practice, but no significant effect on patient outcomes. There are some data to suggest that tailoring guideline implementation strategies to overcome identified barriers to change might be a more effective approach than the multifaceted "one size fits all" strategy used in previous studies. Adopting this tailored approach to guideline implementation in future studies may help bridge the current guideline-practice gap and lead to significant improvements in nutrition practices and patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".