Maternal Dietary L-Arginine and Adverse Birth Outcomes in Dar es Salaam, Tanzania
Bibliographic record
Abstract
The amino acid arginine is a physiological precursor to nitric oxide, which is a key mediator of embryonic survival, fetal growth, and pregnancy maintenance. We evaluated the association between consumption of the amino acid arginine and the rate of adverse birth outcomes using data from a double-blind, randomized, placebo-controlled micronutrient supplementation trial among pregnant women in Dar es Salaam, Tanzania (2001-2004). Dietary intakes of arginine were assessed using repeated 24-hour recalls that were administered throughout pregnancy. Participants (n = 7,591) were monitored by research midwives throughout follow-up to assess pregnancy outcomes. Cubic-restricted splines and multivariable log-Poisson regression with empirical standard errors were used to estimate the continuous and categorical associations between arginine intake and adverse birth outcomes. Compared with women within the lowest quintile of arginine intake, those within the highest quintile had 0.79 times the risk of preterm birth before 37 weeks (95% confidence interval: 0.63, 1.00; P = 0.03). The continuous associations of arginine intake with preterm birth before 37 weeks and with preterm birth before 34 weeks were characterized by an initial rapid decrease in risk with increasing intake (P for nonlinearity < 0.01). Arginine intake was not associated with fetal loss or giving birth to infants who were born small for their gestational ages. This data suggest that the association between dietary arginine intake and preterm birth warrants further investigation.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| 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".