Patterns of use of malnutrition risk screening in pediatric populations: A survey of current practice among pediatric hospitals in North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it