Supportive Care for Common Conditions in Small Vulnerable Newborns and Term Infants: The Evidence
Bibliographic record
Abstract
INTRODUCTION: Small vulnerable newborns (SVNs) are at an increased risk of early death and other morbidities. Essential interventions provided to SVN, and other high-risk newborns have been proven critical in improving their outcomes. We aimed to provide an update on the effectiveness and safety of these interventions in low- and middle-income countries (LMICs). METHOD: Following a comprehensive literature scope, we updated or reanalyzed LMIC-specific evidence for essential SVN care interventions. RESULTS: A total of 113 individual LMIC studies were identified. Most of them were of high risk of bias. Kangaroo mother care significantly reduced SVN's mortality by discharge. Early erythropoiesis stimulating agent lowered SVN's risk of receiving blood transfusion. Prophylactic oral or intravenous ibuprofen resulted in a decreased risk of patent ductus arteriosus in SVN. But it did not have a significant effect on mortality and led to a higher risk of gastrointestinal bleeding. No pooled LMIC data were available for universal screening of hyperbilirubinemia in high-risk newborns. Sunlight therapy had no effect in treating hyperbilirubinemia but increased the risk of hyperthermia. Reflective curtains with phototherapy resulted in a greater and faster decline in bilirubin than standard phototherapy in treating hyperbilirubinemia. Early child development interventions were shown to have a favorable effect on cognitive and motor scores in SVN. The evidence for family involvement and family support was limited and uncertain. CONCLUSION: We present the most updated LMIC evidence for interventions targeting SVN. Despite their effectiveness and safety in improving certain neonatal outcomes, further high-quality trials are required.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".