Modifiers of the Effect of Maternal Multiple Micronutrient Supplementation: an individual patient data meta‐analysis of 17 randomized trials
Bibliographic record
Abstract
Objective To identify individual‐level effect modifiers of the effect of multiple micronutrient supplementation (MMS) containing iron and folic acid (IFA) during pregnancy, compared to IFA alone, on the risk of stillbirth, infant mortality, and birth outcomes. Methods We performed an individual patient data meta‐analysis. Study‐specific estimates were generated, and we pooled subgroup estimates using fixed effects models. Findings We included 17 randomized controlled trials (including 112,953 pregnancies). MMS resulted in greater reductions in low birthweight (RR 0.81; 95% CI: 0.74–0.89), small‐for‐gestational age births (RR 0.91; 95% CI: 0.87–0.96), and six‐month mortality (RR: 0.71; 95% CI: 0.60–0.86) among anemic (hemoglobin <110g/L) as compared with non‐anemic pregnant women. MMS had a greater impact on preterm birth among underweight pregnant women (body mass index <18.5kg/m 2 ) (RR: 0.84; 95% CI: 0.78–0.90) compared to those with BMI ≥18.5 (RR: 0.94; 95% CI 0.90–0.98) (p value heterogeneity 0.01). MMS provided significantly greater reductions in infant mortality for female (RR: 0.87; 95% CI: 0.77–0.99) as compared to male infants (RR: 1.05; 95% CI: 0.93–1.18) (p value heterogeneity 0.04). In general, the survival and birth outcome effects of MMS were greater with high adherence (≥95%) to supplementation. Conclusion MMS produced greater birth outcome benefits for pregnant women with indicators of nutritional deficiency and improved survival for female infants. Support or Funding Information None.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.054 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".