Update to the pediatric Subjective Global Nutritional Assessment (SGNA)
Bibliographic record
Abstract
Lack of a standardized method of identifying and defining pediatric malnutrition has led to an inability to fully understand the prevalence of and impact that malnutrition has on pediatric patients and the healthcare system. The Subjective Global Nutritional Assessment (SGNA) is an assessment tool meant to determine presence and severity of malnutrition in pediatric populations. However, the anthropometric section of the tool contains some out-dated parameters. This has limited its clinical practicality. The aim of this paper is to propose updates to the anthropometrics section of the SGNA. A retrospective analysis of 153 SGNA's performed on children aged 1 month to 16 years was completed, comparing the original SGNA results to SGNA results incorporating updated anthropometric parameters for percentiles and ideal body weight. The category of length/height for age was updated to include z score cutoffs rather than percentiles, and ideal body weight was updated to z scores for weight for length or body mass index (BMI). Two serial growth questions were updated in wording only, to reflect z score trends. The results of the analysis showed these updates would have changed the rankings of eight patients (5%) for length/height for age, and 20 patients (13%) for ideal body weight to weight for length or BMI. Adjustments to these questions did not impact the overall SGNA rating. This study shows updates to the SGNA are not expected to have a significant impact on the validity of the tool and has the potential to improve its applicability to current day practice.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".