Associations between Gestational Weight Gain Adequacy and Neonatal Outcomes in Tanzania
Bibliographic record
Abstract
INTRODUCTION: Gestational weight gain (GWG) is associated with fetal and newborn health; however, data from sub-Saharan Africa are limited. METHODS: We used data from a prenatal micronutrient supplementation trial among a cohort of human immunodeficiency virus-negative pregnant women in Dar es Salaam, Tanzania to estimate the relationships between GWG and neonatal outcomes. GWG adequacy was defined as the ratio of the total observed weight gain over the recommended weight gain based on the Institute of Medicine body mass index-specific guidelines. Neonatal outcomes assessed were stillbirth, perinatal death, preterm birth, low birthweight, macrosomia, small-for-gestational age (SGA), large-for-gestational age (LGA), stunting at birth, and microcephaly. Modified Poisson regressions with robust standard error were used to estimate the relative risk of newborn outcomes as a function of GWG adequacy. RESULTS: Of 7,561 women included in this study, 51% had severely inadequate (<70%) or inadequate GWG (70 to <90%), 31% had adequate GWG (90 to <125%), and 18% had excessive GWG (≥125%). Compared to adequate GWG, severely inadequate GWG was associated with a higher risk of low birthweight, SGA, stunting at birth, and microcephaly, whereas excessive GWG was associated with a higher risk of LGA and macrosomia. CONCLUSION: Interventions to support optimal GWG are needed and may contribute to preventing adverse neonatal outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".