Length‐for‐age (HAZ) progression from the 1st to the 4‐6th month among rural and urban infants in the Western Highlands of Guatemala: longitudinal and cross‐sectional perspectives (620.11)
Bibliographic record
Abstract
Background: We have recently signaled the poorly recognized high prevalence of stunting at birth ‐ a challenge for prevention of linear growth retardation. Objectives: To assess and compare the progression of mean HAZ and stunting rates between the 1st and the 4th‐6th mo in rural R and urban U infants in the Western Highlands of Guatemala. Methods: Two field studies in the Province of Quetzaltenango ‐ Mam‐Mamas (a saturation survey in 8 R Mam‐Mayan‐speaking communities) and Xela‐Babies (a convenience sample from an U health clinic) ‐ included anthropometry measurement in the first 45 and at 131‐182 days of life, in a longitudinal format in both locations and in a transverse manner at the R site only. Stunting was defined as <‐2 SD of HAZ (2006 WHO growth standards). Results: In the cohort series, the HAZ declined at the R site from an initial median of ‐1.61 (stunting prevalence 34.9%) to ‐1.79 (39.8%) (n=129; ΔHAZ ‐0.009±0.070 units/wk); and rose at the U site from ‐1.45 (25.0%) to ‐1.38 (28.3%) (n=60; ΔHAZ +0.011±0.046 u/wk). The stunting prevalence was significantly higher at 4‐6 mo at the R vs U site (p=0.012); HAZ progression was not statistically significant at either site. In the cross‐sectional samples from the R site, the progression was ‐1.89 (40.8%) (n=71) to ‐2.06 (50%) (n=60; ΔHAZ ‐0.010 u/wk). Conclusions: This, to our knowledge, is the first report on the progression of linear growth failure within the first 6 mo of life. Within our study setting, stunting begins in utero, and is worse at the R site, with no statistically significant progression in HAZ scores between the first 6 mo of life. Grant Funding Source : Supported by Graduate Women in Science, McGill Univ Grad Travel Award, GHR‐CAPS Doctoral Fellowship
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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 | 0.001 | 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".