The community-based delivery of an innovative neonatal kit to save newborn lives in rural Pakistan: design of a cluster randomized trial
Bibliographic record
Abstract
BACKGROUND: Worldwide, an estimated 2.9 million neonatal deaths occurred in 2012, accounting for 44% of all under-five deaths. In Pakistan, more than 200,000 newborns die annually and neonatal mortality rates are higher than in any other South Asian country and haven't changed over the last three decades. The high number of neonatal deaths highlights the urgent need for effective and sustainable interventions that target newborn mortality in Pakistan. METHOD/DESIGN: This cluster randomized trial aims at evaluating the impact of delivering an integrated neonatal kit to pregnant women during the third trimester of pregnancy and providing education on how to use the contents (intervention arm) compared to the current standard of care (control arm) in the district of Rahimyar Khan, Punjab province, Pakistan. The kit, which will be distributed through the national Lady Health Worker program, comprises a clean delivery kit (sterile blade, cord clamp, clean plastic sheet, surgical gloves and hand soap), sunflower oil emollient, chlorhexidine, ThermoSpot™, Mylar infant sleeve, and a reusable instant heat pack. Lady health workers will be provided with a standard portable hand-held electric weighing scale. The primary outcome measure is neonatal mortality (death in the first 28 days of life). DISCUSSION: While many cost-effective, evidence-based interventions to save newborn lives exist, they are not always accessible nor have they been integrated into a portable kit designed for home-based implementation entirely by caregivers. The implementation of cost-effective, portable, and easy-to-use interventions has tremendous potential for sustainably reducing neonatal mortality and long-term improvements in population health. The bundling of interventions and commodities together also has much potential for cost-effective delivery and maximizing gains from points of contact. This study will provide empirical evidence on the feasibility and effectiveness of the delivery of an innovative neonatal kit to pregnant women in Pakistan. Together, these findings will help inform policy on the most appropriate interventions to improve newborn survival. TRIAL REGISTRATION: ClinicalTrial.gov NCT02130856. Registered May 1, 2014.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".