Erythropoietin treatment in premature newborns reduces the odds of intraventricular hemorrhage by 97%
Bibliographic record
Abstract
Abstract Intraventricular hemorrhage (IVH) is the most frequent neurological complication in preterm infants, affecting 20-30% of infants born before 32 weeks of gestational age and/or weighing less than 1,500 grams. IVH can lead to long-term neurological sequelae, including cerebral palsy, seizures, posthemorrhagic hydrocephalus, and cognitive deficits. Therefore, mitigating the risk of IVH in neonates is a clinical priority. In this study, we have evaluated whether the hormone erythropoietin (EPO), known for its impact on neuroprotection and stimulation of brain maturation, can be used in IVH prevention in premature infants (<33 gestational weeks). So far, EPO's efficacy in treating preterm infants with IVH remains controversial. While repeated low doses of EPO showed a reduction in the incidence of adverse outcomes, high EPO doses showed no appreciable difference in the frequency of brain injury. In light of these divergent outcomes, in this pilot study, we tested whether low doses of EPO (400 IU/kg), administered intravenously three times per week until reaching 33 weeks of gestationally corrected age, can prevent IVH. Our results show that EPO reduces the odds of IVH among premature babies by 97%; however, it fails to reverse the condition once the injury has developed. These results have crucial clinical importance in preventing IVH in preterm 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 | 0.000 | 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) | 0.002 | 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".