Research developments in pediatric intensive care nutrition: A research intelligence review
Bibliographic record
Abstract
BACKGROUND & AIMS: Pediatric intensive care nutrition is a growing research field on the intersection of three large research domains: Pediatrics, Critical Care Medicine and Nutrition & Dietetics. This study, using Research Intelligence tools, such as bibliometric network visualization software, was designed to map the developments in research topics and collaboration in the field over the past thirty years, and discuss how these developments align with recent recommendations and guidelines of the active expert groups in the field. METHODS: We searched the Web of Science Core Collection for relevant full articles, reviews, letters and proceedings papers. To describe the research field a search strategy was iteratively designed based on the combinations of relevant key words. The articles the were found were processed using software designed for bibliometric network mapping. Filters were applied to select only the most relevant articles for the field. RESULTS: The resulting visualizations show the network of researchers active in the field of pediatric intensive care nutrition, and the collaborations between them. Using the most frequently used key terms a map was created to show the most prominent research areas within the field, and the development of attention for these topics over time. CONCLUSIONS: The network analyses show a research field that is gaining momentum, with several cores of research activity in different institutions. Some research groups collaborate on specific topics within the field, while others seem to be more isolated. The analyses uncover the potential for future collaborations and emerging topics of attention in the different areas of research in the field. The results are compared to recent recommendations for research priorities by active networks in the field. We discuss similarities and discrepancies.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".