Practice Guidelines for Enteral Nutrition Management in Dysglycemic Critically Ill Patients: A Relook for Indian Scenario
Bibliographic record
Abstract
BACKGROUND AND AIM: Intensive-care practices and settings differ for India in comparison to other countries. While guidelines are available to direct the use of enteral nutrition (EN), there are no recommendations specific to nutritional management of EN in dysglycemic patients, specific to patients in Indian critical care settings. Advisory board meetings were arranged to develop the practice guidelines specific to the Indian context, for the use of EN in dysglycemic critically ill patients and to overcome challenges in this field. MATERIALS AND METHODS: Two advisory board meetings were organized to review various existing guidelines, meta-analyses, randomized controlled trials (RCTs), controlled trials and review articles, for their contextual relevance and strength. Three rounds of Delphi voting were done to arrive at consensus on certain recommendations. A systematic grading of practice guidelines by the advisory board was done based on strength of the consensus voting and reviewed supporting evidences. RESULTS: Based on the literature review, the recommendations for developing the practice guidelines were made as per the grading criteria agreed upon by the advisory board. The recommendations were to address challenges regarding prediction and assessment of dysglycemia (DG), acceptable glycemic targets in such settings, general nutritional aspects pertaining to DG nutrition, and nutrition in various superspecialty cases in critical care settings, where DG is commonly encountered. CONCLUSION: This paper summarizes the optimum EN practices for managing DG in critically ill patients. The practical solutions to overcome the challenges in this field are presented as practice guidelines at the end of each section. These guidelines are expected to provide guidance for EN management in dysglycemic critically ill patients. These guidelines also outline the model glycemic control task force and its roles in nutrition care as well as an intensive care unit DG nutrition protocol. HOW TO CITE THIS ARTICLE: . Practice Guidelines for Enteral Nutrition Management in Dysglycemic Critically Ill Patients: A Relook for Indian Scenario. Indian J Crit Care Med 2019;23(12):594-603.
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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 | 0.042 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".