Stunting at birth: recognition of early-life linear growth failure in the western highlands of Guatemala
Bibliographic record
Abstract
OBJECTIVE: Measurements of length at birth, or in the neonatal period, are challenging to obtain and often discounted for lack of validity. Hence, classical 'under-5' stunting rates have been derived from surveys on children from 6 to 59 months of age. Guatemala has a high prevalence of stunting (49.8%), but the age of onset of growth failure is not clearly defined. The objective of the study was to assess length-for-age within the first 1.5 months of life among Guatemalan infants. DESIGN: As part of a cross-sectional observational study, supine length was measured in young infants. Mothers' height was measured. Length-for-age Z-scores (HAZ) were generated and stunting was defined as HAZ <-2 using WHO growth standards. SETTING: Eight rural, indigenous Mam-Mayan villages (n 200, 100% of Mayan indigenous origin) and an urban clinic of Quetzaltenango (n 106, 27% of Mayan indigenous origin), Guatemala. SUBJECTS: Three hundred and six newborns with a median age of 19 d. RESULTS: The median rural HAZ was -1.56 and prevalence of stunting was 38%; the respective urban values were -1.41 and 25%. Linear regression revealed no relationship between infant age and HAZ (r = 0.101, r(2) = 0.010, P = 0.077). Maternal height explained 3% of the variability in HAZ (r = 0.171, r(2) = 0.029, P = 0.003). CONCLUSIONS: Stunting must be carried over from in utero growth retardation in short-stature Guatemalan mothers. As linear growth failure in this setting begins in utero, its prevention must be linked to maternal care strategies during gestation, or even before. A focus on maternal nutrition and health in an intergenerational dimension is needed to reduce its prevalence.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".