Addressing the Burden of Prematurity and Its Prevention in Developing Countries: Is it Time to Act Now?
Bibliographic record
Abstract
Around 3.5 million preterm births occur in India annually, accounting for about 25% of all preterm births worldwide. Prematurity poses a significant burden world-wide but leaves a special impact on the families and health systems of low-socioeconomic countries where easy and universal access to adequate health services is compromised. Premature birth imposes a significant risk of neonatal death among all under-fives due to organ immaturity, sepsis, and respiratory distress syndrome. Efforts to address prematurity on a global scale involve a multifaceted approach, including improving access to quality prenatal care, enhancing neonatal care services, and implementing public health interventions to reduce the infant mortality rate. Despite ongoing challenges, progress has been made in reducing the global burden of prematurity through concerted efforts by governments, international organizations, healthcare professionals, and community stakeholders. However, continued intervention, monitoring, and commitment are needed to further prevent the delivery of preterm babies and to improve their overall health, aiming at reducing the burden on families, caregivers, and the health sector budget worldwide.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".