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Record W3112849548 · doi:10.1097/md.0000000000023528

Effects of intermittent feeding versus continuous feeding on enteral nutrition tolerance in critically ill patients

2020· article· en· W3112849548 on OpenAlexaff
Yuanli Li, Jin Yang, Shunxia Sun, Juan Huang, Aiguo Zhang, Xiaoling Tang

Bibliographic record

VenueMedicine · 2020
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsParenteral nutritionMedicineCritically illCochrane LibraryIntensive care medicineRandomized controlled trialEnteral administrationMeta-analysisMEDLINECritical illnessInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Nutritional support is an indispensable treatment for critically ill patients. Enteral nutrition intolerance is one of the obstacles to the smooth progress of enteral nutrition.Enteral nutrition can be divided into continuous feeding and intermittent feeding. However, the effectiveness and safety of the 2 ways of nutrition infusion are controversial clinically. Therefore, this meta-analysis further evaluated the effect of intermittent feeding versus continuous feeding on enteral nutrition tolerance in critically ill patients. METHODS: Cochrane Library, PubMed, Web of Science, EMbase, China Biology Medicine disc (CBM), China Science and Technology Journal Database (VIP), China Journal full-text Database (CNKI), and Wanfang Database were searched for all randomized controlled trials (RCTs) on the effects of intermittent and continuous feeding on enteral nutrition tolerance in critically ill patients. The quality of literatures was strictly evaluated and the data were extracted by 2 investigators. Meta-analysis was carried out by applying RevMan 5.5 software. RESULTS: The results of this meta-analysis are published in peer-reviewed journals. CONCLUSIONS: This study provides reliable evidence-based support for the effects of intermittent and continuous feeding on enteral nutrition tolerance in critically ill patients. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/4BP5X.

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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.300
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2020
Admission routes1
Has abstractyes

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