A Practical Approach to Identifying Pediatric Disease‐Associated Undernutrition
Bibliographic record
Abstract
ABSTRACT: Disease-associated undernutrition (DAU) is still common in hospitalized children and is generally accepted to be associated with adverse effects on disease outcomes; hence making proper identification and assessment essential in the management of the sick child. There are however several barriers to routine screening, assessment, and treatment of sick children with poor nutritional status or DAU, including limited resources, lack of nutritional awareness, and lack of agreed nutrition policies. We recommend all pediatric facilities to 1) implement procedures for identification of children with (risk of) DAU, including nutritional screening, criteria for further assessment to establish diagnosis of DAU, and follow-up, 2) assess weight and height in all children asa minimum, and 3) have the opportunity for children at risk to be assessed by a hospital dietitian. An updated descriptive definition of pediatric DAU is proposed as "Undernutrition is a condition resulting from imbalanced nutrition or abnormal utilization of nutrients which causes clinically meaningful adverse effects on tissue function and/or body size/composition with subsequent impact on health outcomes." To facilitate comparison of undernutrition data, it is advised that in addition to commonly used criteria for undernutrition such as z score < -2 for weight-for-age, weight-for-length, or body mass index <-2, an unintentional decline of >1inthese z scores over time should be considered as an indicator requiring further assessment to establish DAU diagnosis. Since the etiology of DAU is multifactorial, clinical evaluation and anthropometry should ideally be complemented by measurements of body composition, assessment of nutritional intake, requirements, and losses, and considering disease specific factors.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".