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Record W2280591628 · doi:10.1177/0884533616629633

Gastric Versus Small Bowel Feeding in Critically Ill Adults

2016· review· en· W2280591628 on OpenAlexaboutno aff
Kirsten Martine Schlein

Bibliographic record

VenueNutrition in Clinical Practice · 2016
Typereview
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineParenteral nutritionCritically illIntensive care medicineEnteral administrationIntensive care unitFeeding tubeShort bowel syndromeClinical nutritionIntensive carePopulationSurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Critically ill patients often require enteral feedings as a primary supply of nutrition. Whether enteral nutrition (EN) should be delivered as a gastric versus small bowel feeding in the critically ill patient population remains a contentious topic. The Society of Critical Care Medicine (SCCM)/American Society for Parenteral and Enteral Nutrition (ASPEN), the European Society for Parenteral and Enteral Nutrition (ESPEN), and the Canadian Clinical Practice Guidelines (CCPG) are not in consensus on this topic. No research to date demonstrates a significant difference between the two feeding routes in terms of patient mortality, ventilator days, or length of stay in the intensive care unit (ICU); however, studies provide some evidence that there may be other benefits to using a small bowel feeding route in critically ill patients. The purpose of this paper is to examine both sides of this debate and review advantages and disadvantages of both small bowel and gastric routes of EN. Practical issues and challenges to small bowel feeding tube placement are also addressed. Finally, recommendations are provided to help guide the clinician when selecting a feeding route, and suggestions are made for future research.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.155
GPT teacher head0.478
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations21
Published2016
Admission routes1
Has abstractyes

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