Current red blood cell and platelet transfusion practices in <scp>Canadian</scp> neonatal intensive care units
Bibliographic record
Abstract
BACKGROUND: Red blood cell and platelet transfusions are often prescribed in preterm infants. Consensus on the best transfusion practices in this population has still not been reached, causing disparities in neonatal care. The objective of this study is to provide updated data on current red blood cell and platelet transfusion practices in preterm neonates regarding thresholds and justifications associated with the decision to transfuse. STUDY DESIGN AND METHODS: An electronic survey was sent to one neonatology representative of each center of the Canadian Neonatal Network (31 sites). Descriptive data from each center was collected from the Network's work database. RESULTS: More than half of the respondents (54.8%) have a red blood cell transfusion protocol, while only 32.3% of centers have a platelet transfusion protocol. The most commonly reported justification for red blood cell transfusion is low levels of hemoglobin (100%), while the severity of illness and hemodynamic instability were also frequently mentioned (58.1% and 35.5%). For platelet transfusion justifications, the most reported is low platelet count (96.8%), followed by significant bleeding (93.6%) and severity of illness (35.5%). The reported thresholds also vary greatly depending on the presence of oxygen support for red blood cell transfusions and the occurrence of bleeding or invasive procedures for platelets. DISCUSSION: There is great variability between Canadian centers concerning red blood cells and platelet transfusions in preterm neonates. Practice guidelines should be established for better oversight of transfusion practices in preterm infants to support more judicious use of blood products.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".