Ending preventable newborn deaths in a generation
Bibliographic record
Abstract
The end of the Millennium Development Goal (MDG) era was marked in 2015, and while maternal and child mortality have been halved, MGD 4 and MDG 5 are off-track at the global level. Reductions in neonatal death rates (age <1 month) lag behind those for post-neonates (age 1-59 months), and stillbirth rates (omitted from the MDGs) have been virtually unchanged. Hence, almost half of under-five deaths are newborns, yet about 80% of these are preventable using cost-effective interventions. The Every Newborn Action Plan has been endorsed by the World Health Assembly and ratified by many stakeholders and donors to reduce neonatal deaths and stillbirths to 10 per 1000 births by 2035. The plan provides an evidence-based framework for scaling up of essential interventions across the continuum of care with the potential to prevent the deaths of approximately three million newborns, mothers, and stillbirths every year. Two million stillbirths and newborns could be saved by care at birth and care of small and sick newborns, giving a triple return on investment at this key time. Commitment, investment, and intentional leadership from global and national stakeholders, including all healthcare professionals, can make these ambitious goals attainable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".