Assessment of Nutritional Status Based on STRONGkids Tool in Iranian Hospitalized Children
Bibliographic record
Abstract
Background & Objective: Malnutrition is very common in hospitalized children and is associated with related clinical consequences such as increased risk of infections, increased muscle loss, impaired wound healing, longer hospital stay and higher morbidity and mortality. The estimated prevalence of acute malnutrition in hospitalized children varies from 6.1 to 40.9% in different countries. The current study was conducted with the aim of evaluating the efficiency of STRONGkids (Screening Tool for Risk On Nutritional Status and Growth) tool for assessing malnutrition in hospitalized children in Iran. Methods: All children older than 28 days admitted to the pediatric hospital (Dr. Sheikh, Mashhad, Iran) were enrolled in this study and the screening tool named STRONGkids was applied for them. The anthropometric measurements were measured by a trained operator using standard methods and equipments. The children were classified in three groups of being at high risk, moderate risk and low risk of malnutrition. Results: According to STRONGkids score; 17% of children were classified as low risk, 75% as moderate risk and 8% as high risk group. According to WFH, HFA and WFA z-scores31.4%, 19.2% and 28% of children were identified as moderately and severely malnourished respectively. According to MUAC cut-offs, 3.4% of children were classified as having moderate malnutrition and there was no child with severe malnutrition. Conclusion: It is very important to recognize the nutritional status of the children as early as possible because of its effects on children’s growth. Therefore, evaluating the nutritional status of the hospitalized children is an essential step in clinical assessment. We suggest to apply the STRONGkids score aside with other clinical and anthropometric data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".