When did the substantial loss of child linear growth occur?
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies show that the height-for-age Z-scores (HAZ) falter dramatically shortly after birth until the end of the first two years. Understanding these changes in linear growth in the first two years can help us understand the critical period of child linear growth and propose interventions. OBJECTIVES: This study objectives were to describe the pattern of linear growth faltering and analyze the changes in length-for-age Z-scores (LAZs) throughout the first two years based on birthweight and length status. METHODS: This study analyzed 408 children, participants in Longitudinal Study on Child Growth and Development in Bogor, Indonesia. The linear growth pattern was described based on birthweight and length status. Birthweight and length status was categorized into normal and Small for Gestational Age (SGA). Changes in LAZs (Δ LAZs) in 0-6 months, 6-12 months, and 12-23 months were calculated. General Linear Model Univariate analysis was conducted to analyze the difference of Δ LAZ between SGA and normal children. RESULTS: Though full-term SGA children have significantly higher linear growth velocity during the first 6 months of the infancy period, full-term SGA children could not catch up with the attained growth/height of normal children throughout the first two years. Thus, full-term SGA children ended up with a higher prevalence of stunted. Both in SGA and normal children, the substantial loss of LAZ occurred between 0-6 months. CONCLUSION: The finding in this study showed that the first 1000 days of life is still the best period in stunting prevention; however, the stunting prevention program should start earlier, focusing on the first 500 days of life, and potentially the prenatal period.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".