Effects of intermittent feeding versus continuous feeding on enteral nutrition tolerance in critically ill patients
Bibliographic record
Abstract
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.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".