Assessing the Prevalence and Treatment of Malnutrition in Hospitalized Children in Mofid Children's Hospital During 2015-2016.
Bibliographic record
Abstract
BACKGROUND: Malnutrition in hospitalized patients causes problems in treatment and increases hospitalization duration. The aim of this research was to determine the prevalence of malnutrition in hospitalized children. METHODS: Children aged 1 month to 18 years (n = 1186) who were admitted to medical and surgery wards of Mofid children's hospital from November 2015 to February 2016, entered the study. We measured different anthropometric variables in patients with malnutrition. Also, nutritional counseling was performed and three months follow-up was done. RESULTS: Patient data were registered in questionnaires particularly for children 2 years old and less. 597 children under 2 years of age and 607 children over two years entered the study. The data analysis was done by SPSS version 22.0 (Chicago, IL, USA). The t test inferential method was used in comparing variables. P values less than 0.05 were considered statistically significant. Based on the body mass index (BMI) Z score, and in accordance with the World Health Organization (WHO) cut-off, among children over 2 years, 9% were diagnosed as overweight or obese, 54% were within the normal range and 37% were underweight at time of admission. In the underweight group, 43% were mildly, 21.2% were moderately and 35.8% were severely underweight. Based on the weight for length Z score in patients less than 2 years of age at time of admission, 6% were overweight, 60% were in normal range and 34% were underweight. Among children with malnutrition, 21% had mild, 3.0% had moderate and 10% had severe malnutrition. No significant meaningful relation was found between prevalence of malnutrition and severity of illness. In the moderate to severe undernutrition group, nutritionist counseling was done. Comparison of BMI and weight, before and after admission (the baseline and the follow up visits), was done by means of repeated measurements. Comparison of the patient's weight at time of admission with weight at 1, 2 and 3 months after the first nutritional consultation showed statistically meaningful difference (P value < 0.05). CONCLUSION: Growth indices need to be evaluated in every hospitalized child. Nutritional consultation is useful in children with malnutrition. The main purpose of early diagnosis of malnutrition is to prevent its progression, and also to design a useful, applicable and cost-effective nutritional intervention for malnutrition treatment.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".