Stunting at birth: An under‐recognized phenomenon with implications for maternal health and nutrition
Bibliographic record
Abstract
Background Measurements of length at birth or in the neonatal period are challenging to obtain and often discounted for lack of standardization and validity. Classical “under‐five” stunting rates derive from surveys on children from 6 to 59 mo of age. Objective To assess length‐for‐age (LAZ) within the first 1.5 mo of life among infants from urban (U) and rural (R) sites in the Quetzaltenango Province in the Western Highlands of Guatemala. Methods Supine length was measured to the nearest 0.5 cm by standardized anthropometrics on a SECA 210 infantometer. 104 infants (39% F) were enrolled in 8 rural Mam ‐speaking R villages at 2–46 d of life. 106 infants (50% F) were recruited at an urban U health clinic at 4–33 d of life. Stunting was defined as ≤2 SD of LAZ in relation to the 2006 WHO growth standards. Results In R sites, 100% were indigenous ( Mam ); in the U site, 27% were of indigenous ascent and 73% non‐indigenous. The median R LAZ was −1.55 and prevalence of stunting was 36.5%; the respective U values were −1.41, and 25.5%. The Pearson correlation coefficient of LAZ vs maternal stature for combined U+R was r=0.202 (n=210, p=0.02). Conclusion As linear growth failure in this setting begins in utero, its prevention cannot begin at birth, but must be linked to maternal care strategies during gestation, or even before. Funded by the Nestle Foundation, McGill University, and Graduate Women in Science
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".