Systematic review and comparative analysis of pediatric nutrition screening tools validated in Europe and Canada
Bibliographic record
Abstract
Introduction: Nutritional screening is a useful tool for determining the risk of hospital malnutrition; therefore, reviewing the guidelines on its use in the pediatric population is of great importance.Objective: To provide recommendations on the use of nutrition screening tools validated in Canada and Europe in the Colombian pediatric population.Materials and method: A systematic review was conducted using the PRISMA methodology.The quality of the evidence found in the review was assessed using the U.S. Preventive Services Task Force (USPSTF) tool, which was established by the Canadian Task Force on the Periodic Health Examination for assessing preventive actions.Results: Fifteen studies were included in the review as they met the inclusion criteria.In addition, 7 nutrition screening tools were identified (PYMS, iPYMS, PeDiSMART, PNR, STAMP, PMST and STRONGkids).According to guidelines of the European Society for Clinical Nutrition and Metabolism, the PYMS, iPYMS and STRONGkids tools simultaneously assess prognostic variables such as current nutritional status, stability, expected improvement or worsening of the condition, and the influence of the disease process in nutritional deterioration.Regarding concurrent validity, data analysis shows that PYMS, iPYMS and PMST have sensitivities >85%, and that PYMS has a specificity >85%.In terms of reproducibility, PEDISMART, STRONGkids, STAMP and PYMS have an acceptable interobserver agreement (k>0.41). Conclusion:Based on the evidence found, which was analyzed in terms of prognostic variables, concurrent validity and reproducibility, the use of the PYMS tool in the clinical practice is suggested.In contrast, hospitals must assess the applicability of the STAMP and iPYMS tools.
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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 | 0.027 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.019 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".