Caffeine for the care of preterm infants in sub-Saharan Africa: a missed opportunity?
Bibliographic record
Abstract
In 2019, 2.4 million neonates (infants <28 days of age) died globally. Of these, over 80% were preterm infants (<37 weeks gestation), with the majority born in low-income and middle-income countries.1 Complications of preterm birth, largely from respiratory distress syndrome due to surfactant deficiency, pneumonia or apnoea of prematurity (AOP), are now the leading cause of under 5 mortality globally.1 These conditions are frequently fatal in the absence of effective ventilatory support which is commonplace in neonatal units across sub-Saharan Africa. Although the global neonatal mortality rate (NMR) has halved over the past three decades, significant regional disparities remain. These correlate with World Bank and International Monetary Fund estimates of the proportion of the population living on less than US$1.90 a day, with the majority of poorer countries being in sub-Saharan Africa.1 2 As the region with the highest NMR of 27 per 1000 live births, it is estimated that a baby born in in sub-Saharan Africa is 10 times more likely to die than one born in a high income country.1 Countries in sub-Saharan Africa are unlikely to meet the global target of no more than 12 newborn deaths per 1000 live births by 2030.3 In 2017, 75 countries (almost half from sub-Saharan Africa) signed up to the ‘Every Newborn Action Plan’ that has strategic global and national actions and milestones to address gaps in maternal and newborn care.4 This ambitious commitment requires evidence-based interventions5 and innovative strategies to improve neonatal survival and longer-term outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".