Malnutrition in Hospitalized Children: Prevalence, Impact, and Management
Bibliographic record
Abstract
PURPOSE: Malnutrition in hospitalized children has been reported since the late 1970s. The prevalence of acute and chronic malnutrition was examined in hospitalized patients in a general pediatric unit, and the impact and management of malnutrition were assessed. METHODS: The nutritional risk score (NRS) and nutritional status (NS) (weight, height, body mass index, and skinfold thickness) of children aged zero to 18 years were assessed upon hospital admission. Growth and energy intake were monitored every three days until discharge. RESULTS: A total of 173 children (median age three years, 88 girls) participated; 79.8% had a moderate to severe NRS and 13.3% were acutely and/or chronically malnourished. A high NRS was associated with a longer hospital stay in children older than three years (P<0.05), while a poor NS (weight for height percentile) was correlated with prolonged hospitalization in children aged three years or younger (P<0.05). Although weight did not change during hospitalization, a decrease in skinfolds was documented (n=43, P<0.05). Patients with a high NRS had lower energy intake than those not at risk. However, children with abnormal NS received 92.5% of recommended energy intake. CONCLUSIONS: This study suggests that all children admitted to hospital should have an evaluation of their NRS and NS, so that they can receive appropriate nutrition interventions provided by a multidisciplinary nutrition team.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".