Prevalence of Hospital Malnutrition at Admission and Outcomes in Pediatric Patients
Bibliographic record
Abstract
Background: Hospitalized children are at risk of malnutrition and vulnerable for many adverse outcomes. Objectives: This study aimed to determine the prevalence of hospital malnutrition in pediatric patients admitted at Chiang Mai University (CMU) hospital and evaluate correlation between malnutrition and outcomes including length of hospital stay (LOS), total hospital cost and mortality. Methods and Study Design: A prospective cohort study was conducted at CMU hospital. Patients aged 1 month to 15 year-old who admitted to general pediatric wards were included. Demographic data, anthropometric assessments including weight, length/height and outcomes were collected. Malnutrition was classified by the WHO growth reference. Results: A total of 217 patients with mean age 68.8 ± 53.8 month-old were analyzed. Majority of them were male (65.4%) while leading diagnosis were oncologic, infectious and congenital heart diseases. The prevalence of all malnutrition was 59.9%. According to the WHO classification, percentages of the patients who were stunted, wasted, both of stunted and wasted, and overweight were 29.9%, 9.2%, 17.1%, and 3.7%, respectively. Moreover, compared to previous study of this center in 1985, more than half of hospitalized children have still assessed as under-malnourished patients. For the hospital outcomes, wasting regardless of stunting had significantly longer LOS (8 vs 5 days, p = 0.001) and higher hospital expenditure (37,283.0 vs 23,630.0 Baht, p = 0.004) while mortality was not different. Conclusions: The prevalence of malnutrition in hospitalized children is common and remains unchanged. Acute malnutrition significantly impact on total hospital cost and prolong LOS comparing with other groups
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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 | 0.001 |
| 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) | 0.002 | 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".