Pediatric screening tools for malnutrition: an update
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is ongoing interest in nutritional screening tools in pediatrics to facilitate the identification of children at risk for malnutrition who need further assessment and possible nutritional intervention. The choice for a specific tool depends on various factors. This review aims to provide an overview of recent progress in pediatric nutritional screening methods. RECENT FINDINGS: We present recent studies about newly developed or adjusted tools, the applicability of nutritional screening tools in specific populations, and how to implement screening in the overall process of improving nutritional care in the pediatric hospital setting. SUMMARY: Three new screening tools have been developed for use on admission to hospital: two for the mixed pediatric hospitalized population and one for infants. A simple weekly rescreening tool to identify hospital-acquired nutritional deterioration was developed for use in children with prolonged hospital stay. Different from most previous studies that only assessed the relationship between the nutritional risk score and anthropometric parameters of malnutrition, new studies in children with cancer, burns, and biliary atresia show significant associations between high nutritional risk and short-term outcome measures such as increased complication rate and weight loss. For implementation of a nutritional care process incorporating nutritional screening in daily practice, simplicity seems to be of great importance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".