Survive and Thrive: Outcomes of Children Enrolled in a Follow-Up Clinic for Small and Sick Newborns in Rural Rwanda
Bibliographic record
Abstract
INTRODUCTION: Children born small or sick are at risk of death and poor development, but many lack access to preventative follow-up services. We assessed the impact of Pediatric Development Clinics (PDC), which provide structured follow-up after discharge from hospital neonatal care units, on children's survival, nutrition and development in rural Rwanda. METHODS: This quasi-experimental study compared a historic control group to children receiving PDC in Kayonza and Kirehe districts. Study populations in both districts included children born preterm or with birthweight < 2000 g and discharged alive. Kirehe additionally included children with hypoxic ischemic encephalopathy (HIE). Home-based cross-sectional surveys were conducted in Kayonza among children with expected chronological age 11-36 months in 2014 (controls) and 2018 (PDC group) and in Kirehe among children with expected chronological age 17-39 months in 2018 (controls) and 2019 (PDC group). Outcomes were measured using anthropometrics and the Ages and Stages Questionnaires. We used weighted logistic regression to control for confounding and differential non-participation. RESULTS: = 56) died before the survey. PDC was associated with reduced odds of death (aOR = 0.49, 95% CI: 0.26-0.92) and reduced odds of developmental delay (aOR = 0.48, 95% CI: 0.30-0.77). In Kayonza, PDC was associated with reduced stunting (aOR = 0.52, 95% CI: 0.28-0.98). PDC was not associated with reduced underweight or wasting. CONCLUSIONS: PDC was associated with improved survival and development among children born preterm, with low birthweight, or with HIE. Increased access to PDC, scale-up across Rwanda, and implementation of similar services and early intervention in other low-resource settings could support children born small or sick.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".