Effects of placental transfusion in extremely low birthweight infants: meta‐analysis of long‐ and short‐term outcomes
Bibliographic record
Abstract
BACKGROUND: Risks and benefits of increasing placental transfusion in extremely preterm infants (extremely low birthweight [ELBW], <1000 g) are ill defined. We performed a meta-analysis to compare long- and short-term outcomes of ELBW infants in trials of enhanced placental transfusion regimens. STUDY DESIGN AND METHODS: We conducted a meta-analysis of randomized controlled trials (RCTs) of delayed umbilical cord clamping or umbilical cord milking in compliance with PRISMA and Cochrane Collaborative guidelines for systematic reviews. We searched multiple databases for medical literature up to December 2012. Inclusion criteria were preterm infants less than 30 weeks and less than 1000 g birthweight, randomly assigned to enhanced placental transfusion (either delayed cord clamping or cord milking) versus immediate cord clamping. The primary outcome was standardized neurodevelopmental outcome at 18 to 24 months corrected age using a standardized scale. Several short-term outcomes were also evaluated as secondary variables. RESULTS: We found 19 studies of which 10 studies could be included (n = 199). Three reported neurodevelopmental outcomes, none of which showed significant rates of disability. Two reported these at 18 to 24 months (n = 42) but used different scales preventing pooling. Short-term benefits of enhanced placental strategies included better blood pressure and hemoglobin on admission, along with reduced blood transfusions, a trend to reduced intraventricular hemorrhage, and episodes of late-onset sepsis. CONCLUSIONS: Strategies to enhance placental transfusion may improve short-term outcomes of ELBW infants. However, paucity of data on neurodevelopmental outcomes and safety concerns tempers enthusiasm for these interventions. Appropriately designed RCTs to assess short-term and longterm outcomes are needed in ELBW infants.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.042 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".