Evaluating the Effectiveness of STRONGkids in Identifying Nutritional Risk in Outpatients of Child Health Care Clinics
Bibliographic record
Abstract
Aim: To investigate the value of the STRONGkids tool for screening malnutrition risk among pediatric outpatients in China. Methods: This multicenter, cross-sectional, observational study included pediatric outpatients at the Maternity and Child Health Care Hospital in Changzhou, China, from March 2021 to March 2022. More specifically, we performed anthropometric assessments and screened pediatric patients under 2 years of age for nutritional risk using the STRONGkids tool. Results: The total number of samples assessed for malnutrition risk was 1,062, of which 81.4% (n = 865) were low risk, 16.4% (n = 174) were medium risk, and 2.2% (n = 23) were high risk. In terms of sex, 81.2% (n = 448) of all males included in the present study were classified as low risk, while 16.5% (n = 91) and 2.3% (n = 13) were medium and high risk, respectively. Furthermore, the sensitivity and specificity of STRONGkids were 0.906 and 0.837, respectively, and the AUC was 0.872 (p < 0.01). Conclusion: Although our findings failed to reveal any significant association between malnutrition risk and sex, malnutrition risk was significantly associated with age category and was more likely to occur within the first year of life. The STRONGkids tool demonstrated diagnostic efficacy in screening outpatient children for nutritional risk and could accurately identify children at risk of malnutrition. It promotes children's growth and development, reduces the risk of disease, and is beneficial to long-term health.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".