Exploring Global Research Trends in Enteral Nutrition for Mechanically Ventilated Patients: A Comprehensive Bibliometric Study
Bibliographic record
Abstract
Background: Mechanically ventilated patients are at significant risk of malnutrition, a condition that not only prolongs ICU length of stay but also dramatically increases mortality risk. While existing studies have focused on clinical protocols, the global research landscape and knowledge gaps remain underexplored in terms of systematic synthesis. This study employs systematic bibliometric analysis to quantitatively trace publication trajectories, identify dominant research clusters, and propose evidence-based pathways for enhancing global research prioritization and improving clinical nutrition practices. Methods: Studies published between January 1, 2000, and November 6, 2024, on enteral nutrition in mechanically ventilated patients were identified using the Web of Science platform. A comprehensive analysis of these studies was performed using VOSviewer, CiteSpace, and an online analysis tool(R-bibliometrix), assessing publication trends, contributions from countries/regions and institutions, institutional productivity, major funding agencies, journals and their co-citation networks, authors and their co-citation patterns, and keywords and emerging trends in the field. Results: A total of 1164 studies were included in the analysis, which showed a general upward trend in publication volume over the 24 years. The USA, supported by robust financial resources and well-established research institutions, ranked first in terms of publication count, citation number, and h-index. The top three institutions in terms of publication volume were Harvard University, the University of Toronto, and Queen’s University of Canada. The journals with the highest numbers of publications were the Journal of Parenteral and Enteral Nutrition, Critical Care Medicine , and Clinical Nutrition . The most prolific author was Dr. Daren K. Heyland. The top keywords were “nutrition”, “malnutrition”, and “prevalence”. Conclusion: Research in the field of enteral nutrition for mechanically ventilated patients continues to thrive, increasing the number of investigations. The present bibliometric analysis provides a summary of current research frontiers and hotspots in the field, offering a robust reference framework for researchers investigating enteral nutrition in mechanically ventilated critically ill patients. Keywords: bibliometrics, enteral nutrition, mechanically ventilated, citespace, VOSviewer
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.172 | 0.277 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".