Enhanced iron‐folic acid supplementation program reduces risk of under 5 mortality in Nepal (636.2)
Bibliographic record
Abstract
Objective To investigate the impact of a program to improve coverage of antenatal IFA supplementation on child survival in Nepal using pooled data from 3 Nepal DHS (2001, 2006 and 2011). Methods Survival information from 13,009 singleton most recent live‐born infants within the 5 years prior to each interview was used. The primary outcomes were mortality indicators for under‐5 children and the main exposure variable was implementation of enhanced IFA program. Multivariate Cox proportional hazards regression analyses was conducted and adjusted for X potential confounders and the sampling design. Results After the implementation of enhanced IFA program, early neonatal mortality was significantly reduced by 53% (aHR: 0.47, 95% CI 0.26, 0.84, p=0.012) compared to before the program started. The risk of infant mortality was significantly reduced by 49% (aHR: 0.51, 95% CI 0.31, 0.82, p=0.006), and by 38% (aHR: 0.62, 95% CI 0.39, 0.99, p=0.043) for under‐5 mortality after implementation of the enhanced program. Conclusion A program to improve IFA coverage significantly reduced the risk of under‐5 deaths in Nepal by nearly 40%. The greatest magnitude of risk reduction was for early neonatal deaths.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".