Neonatal and pediatric blood bank practice in the <scp>United States</scp>: Results from the <scp>AABB</scp> pediatric transfusion medicine subsection survey
Bibliographic record
Abstract
BACKGROUND: There are limited standards guiding the selection and processing of blood components specific for neonatal and pediatric transfusions. Therefore, blood banks (BBs) and transfusion services must create their own policies and procedures. STUDY DESIGN AND METHODS: The American Association of Blood Banks (AABB) Pediatric Transfusion Medicine Subsection Committee developed a 74-question survey to capture neonatal and pediatric BB practices in the United States. RESULTS: Thirty-five centers completed the survey: a response rate 15.8%. Responses indicated that most carry a mixed inventory of red blood cells (RBCs); 94.2% allow more than one type of RBC product for small-volume (SV) and large-volume (LV) transfusions to neonatal and pediatric patients. Many had storage age thresholds for RBCs transfused to neonates (SV = 60%, LV = 67.7%) but not older pediatric patients. The use of Group O for nonurgent RBC transfusion in neonates was common (74.2%). Responses related to special processing of RBCs and platelets indicated that 100% RBC and platelets are leukocyte-reduced (LR) for neonates and 97% for non-neonates. Irradiation of RBCs and platelets was commonly performed for neonatal transfusion (88.6%). Providing cytomegalovirus (CMV) seronegative products, volume reduction, and washing were variable. All centers transfused single-donor apheresis platelets; 20% allowed pathogen reduction (PR). The majority of centers have strategies limiting the amount of incompatible plasma transfused; however, few titrate ABO isoagglutinins in plasma-containing products (20% for platelets and 9.1% for plasma). CONCLUSIONS: Variability exists in BB practice for neonatal and pediatric transfusion. Future studies are needed to understand and define best BB practices in these patient populations.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".