What Is New about Transfusions for Preterm Infants? An Update
Bibliographic record
Abstract
Currently the question of whether to maintain a higher hemoglobin level by transfusing more liberally, as opposed to a more restrictive strategy with lower hemoglobin maintenance levels, has not been answered. We review summarized conclusions of a Cochrane systematic review and meta-analysis of 614 infants in 4 randomized controlled trials (RCT) pooling data. This suggests potential benefits of higher hemoglobin levels, i.e., a possible improved cognition of infants at 18-21 months' corrected age and a reduction of apnea. However, the data on cognition is hypothesis generating as it derives from a post hoc analysis from a single trial in 451 infants. Moreover, the data on apnea need confirmation in larger trials. The effect of adding data of cognitive 2-year outcomes of 1,744 infants from 2 RCT, which will be reported soon, should expand our understanding. This new data will need to be integrated with the older generation of RCTs but also with emerging suggestions from observational data on potential risks of blood transfusions. We discuss some of these warnings from observational studies. Finally, we ask whether we are ready to individualize blood transfusion to physiological measures made in individual infants, and we point to some current difficulties hindering this step.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".