Catch-up growth and growth deficits: Nine-year annual panel child growth for native Amazonians in Bolivia
Bibliographic record
Abstract
BACKGROUND: Childhood growth stunting is negatively associated with cognitive and health outcomes, and is claimed to be irreversible after age 2. AIM: To estimate growth rates for children aged 2-7 who were stunted (sex-age standardised z-score [HAZ] <-2), marginally-stunted (-2 ≤ HAZ ≤-1) or not-stunted (HAZ >-1) at baseline and tracked annually until age 11; frequency of movement among height categories; and variation in height predicted by early childhood height. SUBJECTS AND METHODS: This study used a 9-year annual panel (2002-2010) from a native Amazonian society of horticulturalists-foragers (Tsimane'; n = 174 girls; 179 boys at baseline). Descriptive statistics and random-effect regressions were used. RESULTS: This study found some evidence of catch-up growth in HAZ, but persistent height deficits. Children stunted at baseline improved 1 HAZ unit by age 11 and had higher annual growth rates than non-stunted children. Marginally-stunted boys had a 0.1 HAZ units higher annual growth rate than non-stunted boys. Despite some catch up, ∼ 80% of marginally-stunted children at baseline remained marginally-stunted by age 11. The height deficit increased from age 2 to 11. Modest year-to-year movement was found between height categories. CONCLUSIONS: The prevalence of growth faltering among the Tsimane' has declined, but hurdles still substantially lock children into height categories.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".