Patterns of use of malnutrition risk screening in pediatric populations: A survey of current practice among pediatric hospitals in North America
Bibliographic record
Abstract
Information on the use of validated malnutrition risk screening tools in pediatric facilities to guide malnutrition identification, diagnosis, and treatment is scarce. Therefore, a survey of pediatric healthcare facilities and practitioners to ascertain malnutrition risk screening practices in North America was conducted. A pediatric nutrition screening practices survey was developed and sent to members of the American Society for Parenteral and Enteral Nutrition, the Council for Pediatric Nutrition Professionals and the Academy of Nutrition and Dietetics Pediatric Nutrition Practice Group. Respondents represented 113 pediatric hospitals in the United States and six in Canada, of which 94 were inpatient and 59 were outpatient. Nutrition risk screening was completed in 90% inpatient settings, and 63% used a validated screening tool. Nurses performed most malnutrition risk screens in the inpatient setting. Nutrition risk screening was reported in 51% of outpatient settings, with a validated screening tool being used in 53%. Measured anthropometrics were used in 78% of inpatient settings, whereas 45% used verbally reported anthropometrics. Measured anthropometrics were used in 97% outpatient settings. Nutrition risk screening was completed in the electronic health record in 80% inpatient settings and 81% outpatient settings. Electronic health record positive screen generated an automatic referral in 80% of inpatient and 45% of outpatient settings. In this sample of pediatric healthcare organizations, the results demonstrate variation in pediatric malnutrition risk screening in North America. These inconsistencies justify the need to standardize pediatric malnutrition risk screening using validated pediatric tools and allocate resources to perform screening.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".