Placental Transfusion Strategies in Preterm Infants in Low- and Middle-Income Countries: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Placental transfusion strategies in preterm newborns have not been evaluated in low- and middle-income countries (LMICs). The objective of this systematic review was to compare placental transfusion strategies in preterm newborns in LMICs, including delayed cord clamping (DCC) for various time intervals, DCC until cord pulsations stop, umbilical cord milking, and immediate cord clamping (ICC). METHODS: Medline, Embase, CINAHL, and CENTRAL were searched from inception. Observational studies and randomized controlled trials (RCTs) were included. Two authors independently extracted data for Bayesian random-effects network meta-analysis (NMA) if more than 3 interventions reported an outcome or a pairwise meta-analysis was utilized. RESULTS: Among newborns <34 weeks of gestation, NMA of 9 RCTs could not rule out benefit or harm for survival from DCC 30-60 s compared to ICC: relative risk (RR) (95% credible interval) 0.96 (0.78-1.12), moderate certainty, or any included strategy compared to each other (low to very low certainty). Among late preterm newborns, DCC 120 s might be associated with improved survival: RR (95% confidence interval) 1.11 (1.01-1.22), very low certainty. We could not detect differences in the risk of intraventricular hemorrhage grade > II and bronchopulmonary dysplasia for any included intervention (low to very low certainty). DCC 60 s and 120 s might improve the hematocrit level among all preterm newborns (very low certainty), and DCC 45 s may decrease the risk of receipt of inotropes among newborns <34 weeks of gestation (low certainty). CONCLUSIONS: In LMICs, DCC for 60 s and 120 s might improve hematocrit level in preterm newborns, and DCC for 45 s may decrease the risk of receipt of inotropes in newborns <34 weeks, with no conclusive effect on survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".