Characteristics of red blood cell transfusion among very preterm infants in China
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: National-level data on the incidence of red blood cell (RBC) transfusions and outcomes among very preterm infants (VPIs) are lacking in China. This study aims to describe the use and variation of RBC transfusion among VPIs in China. MATERIALS AND METHODS: This cohort study was conducted among 70 tertiary hospitals participating in the Chinese Neonatal Network (CHNN) from 2019 to 2020 across China. All VPIs admitted to the CHNN neonatal intensive care units (NICUs) were included. RESULTS: A total of 13,447 VPIs were enrolled, of whom 7026 (52.2%) received ≥1 RBC transfusions. The mean number of transfusions per infant was 2 (interquartile range [IQR] 1-4 times) and the median age at first transfusion was 15 days (IQR 3-27 days). The transfusion rate was higher in critically ill infants compared with non-critically ill infants (70.5% vs. 39.3%). The transfusion rate varied widely (13.5%-95.0%) between different NICUs. The prevalence of death, severe intra-ventricular haemorrhage, necrotizing enterocolitis (NEC) or spontaneous intestinal perforation (SIP), sepsis, bronchopulmonary dysplasia (BPD), severe retinopathy of prematurity (ROP) and cystic periventricular leukomalacia (cPVL) was significantly higher in the transfused group. Among non-critically ill infants, RBC transfusion was independently associated with BPD, severe ROP and cPVL. CONCLUSION: Our study, providing the first baseline data on RBC transfusions among VPIs in China, shows an alarmingly high RBC transfusion rate with significant site variations. There is an urgent need for national guidelines on RBC transfusions for VPIs in China.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".