Antenatal multiple micronutrient supplements versus iron‐folic acid supplements and birth outcomes: Analysis by gestational age assessment method
Bibliographic record
Abstract
Meta-analyses consistently have found that antenatal multiple micronutrient supplementation (MMS) compared with iron and folic acid (IFA) alone reduce adverse birth outcomes. In 2020, the World Health Organization (WHO) placed a conditional recommendation for MMS and requested additional trials using ultrasounds to establish gestational age, because the evidence on low birthweight (LBW), preterm birth and small for gestational age (SGA) was considered inconsistent. We conducted meta-analyses to determine if the effects of MMS on LBW, preterm birth and SGA differed by gestational age assessment method. Using data from the 16 trials in the WHO analyses, we calculated the effect estimates of MMS versus IFA on birth outcomes (generic inverse variance method and random effects model) stratified by method of gestational age assessment: ultrasound, prospective collection of the date of last menstrual period (LMP) and confirmation of pregnancy by urine test and recall of LMP. The effects of MMS versus IFA on birthweight, preterm birth and SGA appeared consistent across subgroups with no evidence of subgroup differences (p > 0.05). When limited to the seven trials that used ultrasound, the beneficial effects of MMS were demonstrated: risk ratios of 0.87 (95% confidence interval [CI] 0.78-0.97) for LBW, 0.90 (95% CI, 0.79-1.03) for preterm birth and 0.9 (95% CI, 0.83-0.99) for SGA. Sensitivity analyses indicated consistency in the results. These results, together with recent analyses demonstrating comparable effects of MMS (vs. IFA) on maternal anaemia outcomes, strengthen the evidence to support a transition from IFA to MMS programmes in low- and middle-income countries.
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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 | 0.048 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.086 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".