Contribution of growth in fetal length to post‐natal length in Bangladesh children
Bibliographic record
Abstract
Physical growth of children continues during the critical window from conception to 24 mo, punctuated by birth. In Asia especially, prenatal growth deficit is understood to partially explain post‐natal stunting, but this is not well characterized. This study quantified the contribution of pre‐ and post‐natal growth deficits to length at 24 mo using a unique cohort of 1931 children followed from first trimester of gestation to 24 mo from the Maternal Infant Nutritional Interventions Matlab (MINIMat) study in rural Bangladesh. Femur diaphysis length was assessed by trained paramedics at 14, 19, and 30 wk using real‐time ultrasound on a portable machine. Recumbent length was measured at birth then monthly to 12 mo and every 3 mo from 13 to 24 mo using length boards. British reference values (Chitty et al.) for fetal length and from the sex‐specific WHO 2006 growth standards for recumbent length were used to adjust for variation from nominal age. Quintiles formed from length at 24 mo revealed that femur length at 14 wk was unrelated to length at 24 mo. From linear regression with length at 24 mo as outcome, variance explained was 0.0, 1.1, 7.1, 23.2, and 53.2% for models adding sequentially length at 14 wk, 19 wk, 30 wk, birth, and 6 mo. Thus, 23% of variance at 24 mo was explained by fetal growth and 30% by the first 6 mo of post‐natal growth. The critical period of fetal growth for explaining length at 24 mo was the second half of gestation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".