A Retrospective Comparison between the PNST and other Paediatric Nutritional Screening Tools
Bibliographic record
Abstract
Background: Although it is widely acknowledged that hospitalized children are at greater risk of malnutrition, the available paediatric Nutritional Risk Screening (NRS) tools have not yet become universally used to identify those children at greater risk. Furthermore, the utility of one NRS tool over another remains unclear. Materials and Methods: The utility of a recently developed tool, the Paediatric Nutritional Screening Tool (PNST), was evaluated using data previously collected in the assessment of three other NRS tools in 281 children from Iran and New Zealand. The sensitivity and specificity of each tool was then assessed based on the WHO criteria for malnutrition. Results: The PNST recognized about half of the malnourished patients while the other three tools identified at least 85% of these children. The sensitivity of PNST for moderate (BMI-z < 2) and severe malnutrition (BMI-z <-3) was 37% and 46% respectively, while the sensitivity for other three NRS tools ranged from 82-100%. Conclusion: In this data set, the PNST tool did not perform as well as the three more established NRS tools. Further work is required to provide optimal tools for the identification of hospitalized children at risk of malnutrition
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".