Pulmonary Hemorrhage in Premature Infants: Pathophysiology, Risk Factors and Clinical Management
Bibliographic record
Abstract
Pulmonary hemorrhage (PH) is a life-threatening complication predominantly affecting preterm infants, particularly those with very low birth weight (VLBW) and fetal growth restriction (FGR). Typically occurring within the first 72 h of life, PH is characterized by acute respiratory deterioration and significant morbidity and mortality. This review synthesizes current evidence on the multifactorial pathogenesis of PH, highlighting the roles of immature pulmonary vasculature, surfactant-induced hemodynamic shifts, and left ventricular diastolic dysfunction. Key risk factors include respiratory distress syndrome (RDS), hemodynamically significant patent ductus arteriosus (hsPDA), sepsis, coagulopathies, and genetic predispositions. Diagnostic approaches incorporate clinical signs, chest imaging, lung ultrasound, and echocardiography. Management strategies are multifaceted and include ventilatory support-particularly high-frequency oscillatory ventilation (HFOV)-surfactant re-administration, blood product transfusion, and targeted hemostatic agents. Emerging therapies such as recombinant activated factor VII and antifibrinolytics show promise but require further investigation. Preventive measures like antenatal corticosteroids and early indomethacin prophylaxis may reduce incidence, particularly in high-risk populations. Despite advancements in neonatal care, PH remains a major contributor to neonatal mortality and long-term neurodevelopmental impairment. Future research should focus on individualized risk stratification, early diagnostic tools, and optimized treatment protocols to improve outcomes. Multidisciplinary collaboration and innovation are essential to advancing care for this vulnerable population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".