Effect of an integrated neonatal care kit on neonatal health outcomes: a cluster randomised controlled trial in rural Pakistan
Bibliographic record
Abstract
INTRODUCTION: In 2016, 2.6 million children died during their first month of life. We assessed the effectiveness of an integrated neonatal care kit (iNCK) on neonatal survival and other health outcomes in rural Pakistan. METHODS: We conducted a community-based, cluster randomised, pragmatic, open-label, controlled intervention trial in Rahim Yar Khan, Punjab, Pakistan. Clusters, 150 villages and their lady health workers (LHWs), were randomly assigned to deliver the iNCK (intervention) or standard of care (control). In intervention clusters, LHWs delivered the iNCK and education on its use to pregnant women. The iNCK contained a clean birth kit, chlorhexidine, sunflower oil, a continuous temperature monitor (ThermoSpot), a heat reflective blanket and reusable heat pack. LHWs were also given a hand-held scale. The iNCK was implemented primarily by caregivers. The primary outcome was all-cause neonatal mortality. Outcomes are reported at the individual level, adjusted for cluster allocation. Enrolment took place between April 2014 and July 2015 and participant follow-up concluded in August 2015. RESULTS: 5451 pregnant women (2663 and 2788 in intervention and control arms, respectively) and their 5286 liveborn newborns (2585 and 2701 in intervention and control arms, respectively) were enrolled. 147 newborn deaths were reported, 65 in the intervention arm (25.4 per 1000 live births) compared with 82 in the control arm (30.6 per 1000 live births). Neonatal mortality was not significantly different between treatment groups (risk ratio 0.83, 95% CI 0.58 - 1.18; p = 0.30). CONCLUSION: Providing co-packaged interventions directly to women did not significantly reduce neonatal mortality. Further research is needed to improve compliance with intended iNCK use.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".