Trends and Novel Research in Hospital Nutrition Care: A Narrative Review of Leading Clinical Nutrition Journals
Bibliographic record
Abstract
Hospital malnutrition is a longstanding problem that continues to be underrecognized and undertreated. The aim of this narrative review is to summarize novel, solution-focused, recent research or commentary to update providers on the prevention of iatrogenic malnutrition as well as the detection and treatment of hospital malnutrition. A narrative review was completed using the top 11 clinically relevant nutrition journals. Of the 13,850 articles and editorials published in these journals between 2013 and 2019, 511 were related to hospital malnutrition. A duplicate review was used to select (n = 108) and extract key findings from articles and editorials. Key criteria for selection were population of interest (adult hospital patients, no specific diagnostic group), solution-focused, and novel perspectives. Articles were categorized (6 classified in >1 category) as Screening and Assessment (n = 17), Standard (n = 25), Advanced (n = 12) and Specialized Nutrition Care (n = 8), Transitions (n = 15), Multicomponent (n = 21), Education and Empowerment (n = 9), Economic Impact (n = 3), and Guidelines (n = 4) for summarizing. Research advances in screening implementation, standard nutrition care, transitions, and multicomponent interventions provide new strategies to consider for malnutrition prevention (iatrogenic), detection, and care. However, several areas requiring further research were identified. Specifically, larger and more rigorous studies that examine health outcomes and economic analyses are urgently needed.
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 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 | 0.019 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".