Accuracy of international growth charts to assess nutritional status in children and adolescents: a systematic review
Bibliographic record
Abstract
Abstract Objective: To verify, through a systematic review, the accuracy of nutritional assessment in children and adolescents using the length/height-for-age and BMI-for-age growth charts of the Centers for Disease Control and Prevention (CDC) (2000), the World Health Organization (WHO) (2006/2007) and the International Obesity Task Force (IOTF) (2012). Data source: We selected articles from the databases Medical Literature Analysis and Retrieval System Online (MEDLINE), through PubMed, National Library of Medicine and The National Institutes of Health (NIH), Scientific Electronic Library Online (SciELO) and Virtual Health Library (VHL). The following descriptors were used for the search: “Child”, “Adolescent”, “Nutritional Assessment”, “Growth Chart”, “Ethnic Groups”, “Stature by age”, “Body Mass Index”, “Comparison”, “CDC”, “WHO”, and “IOTF”. The selected articles were assessed for quality through the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies of the NIH. Data synthesis: Thirty-three studies published between 2007 and 2020 were selected and, of these, 20 presented good quality, 12 presented fair quality and one presented poor quality. For children under five years old, the WHO length/height-for-age growth charts were shown appropriate for children from Argentina, South Africa, Brazil, Gabon, Qatar, Pakistan and the United States. For those five years old and older, the WHO BMI-for-age growth charts were accurate for the Brazilian and Canadian populations, while the IOTF growth charts were accurate for the European populations. Conclusions: There are difficulties in obtaining international growth charts for children from 5 years old and older that go along with a long period of growth, and which include genetic, cultural and socioeconomic differences of multiethnic populations who have already overcome the secular trend in height.
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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 | 0.030 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".