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Practice Guidelines for Enteral Nutrition Management in Dysglycemic Critically Ill Patients: A Relook for Indian Scenario

2019· article· en· W2999713872 on OpenAlexaff
Ambrish Mithal, Jeetendra Sharma, Atul P Kulkarni, MN Sivakumar, Yatin Mehta, Sanghamitra Chakravarti, Anshu Joshi, B. Ravinder Reddy, Harita Bathina

Bibliographic record

VenueIndian Journal of Critical Care Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicHyperglycemia and glycemic control in critically ill and hospitalized patients
Canadian institutionsNutrition International
Fundersnot available
KeywordsMedicineGrading (engineering)Delphi methodIntensive care medicineCritically illSystematic reviewContext (archaeology)Randomized controlled trialCritical appraisalMEDLINEBest practiceAlternative medicineSurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.373
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2019
Admission routes1
Has abstractyes

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