Nutrition risk prevalence and screening tools' validity in pediatric patients: A systematic review
Bibliographic record
Abstract
Nutrition screening (NS) allows health professionals to identify patients at nutritional risk (NR), enabling early nutrition intervention. This study aimed to systematically review the criterion validity of NS tools for hospitalized non-critical care pediatric patients and to estimate the prevalence of NR in this population. This research was performed using PubMed, Embase, and Scopus databases until June 2021. The reviewers extracted the studies' general information, the population characteristics, the NR prevalence, and the NS tools' concurrent and predictive validity data. Quality evaluation was performed using the Newcastle-Ottawa Scale, adapted Newcastle-Ottawa Scale, and Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). The primary studies were qualitatively analyzed, and descriptive statistics were calculated to describe the NR prevalence. Of the total 3944 studies found, 49 met the inclusion criteria. Ten different pediatric NS tools were identified; the most frequently used were Screening Tool for Risk on Nutritional Status and Growth (STRONGkids), Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP), and Pediatric Yorkhill Malnutrition Score (PYMS). The mean NR prevalence was 59.85% (range, 14.6%-96.9%). Among all NS tools analyzed, STRONGkids and PYMS showed the best diagnostic performance. STRONGkids had the most studies of predictive validity showing that the NR predicted a higher hospital length of stay (odds ratio [OR], 1.96-8.02), health complications during hospitalization (OR, 3.4), and the necessity for nutrition intervention (OR, 18.93). Considering the diagnostic accuracy, robust and replicated findings of predictive validity, and studies' quality, STRONGkids performed best in identifying NR in the pediatric population among the tools identified.
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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 | 0.020 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".