Screening for Pediatric Malnutrition at Hospital Admission: Which Screening Tool Is Best?
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Identifying children at malnutrition risk on admission to hospital is considered best practice; however, nutrition screening in pediatric populations is not common. The aim of this study was to determine which screening tool is able to identify children with malnutrition on admission to hospital. METHODS: A nurse administered 2 pediatric nutrition screening tools, Screening Tool for Risk on Nutritional Status and Growth (STRONGkids) and Pediatric Nutrition Screening Tool (PNST) to patients admitted to medicine and surgery units (n = 165). The Subjective Global Nutritional Assessment (SGNA) was then completed by a dietitian, blinded to the results of the screens. Sensitivity, specificity, and κ were calculated for both screening tools against the SGNA. A receiver operating characteristic (ROC) curve assessed alternate cutoffs for each tool. Length of hospital stay (LOS) was used to assess prospective validity. RESULTS: Using the recommended cutoffs, the sensitivity of STRONGkids was 89%, specificity 35%, and κ 0.483. The sensitivity of PNST was 58%, specificity 88%, and κ 0.601. Using adjusted cutoffs, PNST's sensitivity improved to 87%, specificity 71%, and κ 0.681, and STRONGkids specificity improved to 61%, sensitivity 80%, and κ 0.5. Children identified at nutrition risk had significantly longer LOS (P < 0.05). CONCLUSION: This study showed neither tool was appropriate for clinical use based on published cutoffs. By adjusting the cutoffs using ROC curve analysis, both tools improved overall agreement with the SGNA without significantly impacting the prospective validity. PNST with adjusted cutoffs is the most appropriate for clinical use in this population.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it