A national survey of transfusion‐related acute lung injury risk reduction policies for platelets and plasma in the United States
Bibliographic record
Abstract
BACKGROUND: Little information exists on the specific transfusion-related acute lung injury (TRALI) risk reduction practices used by multiple blood collecting institutions in the United States. STUDY DESIGN AND METHODS: An AABB-appointed TRALI working group designed a set of questions about TRALI risk reduction for platelets (PLTs) and plasma. AABB member institutions were asked to respond via an Internet-based survey during a 3-week period in August through September 2009. RESULTS: Valid responses were received from 47 US blood centers (accounting for 1.57 million apheresis PLT units and 3.15 million whole blood-derived transfusable plasma units) and 56 hospital blood collectors. Among the blood centers, 87 and 98% had initiated some PLT and plasma risk reduction, respectively. HLA antibody testing of plateletpheresis donors was performed by 20 (43%) blood centers. There was substantial variation in the number of pregnancies (from one to more than four) that triggered testing and most centers did not screen based on a transfusion history. Almost all centers had policies to redirect HLA antibody-positive donors to whole blood donation and to potentially retest HLA antibody-negative donors. There were no blood centers performing HNA antibody testing. Sex-based risk reduction policies for plasma included all male, or predominantly male, and never-pregnant females; these varied by blood center, blood group, and method of plasma collection. A majority of centers indicated increased production of plasma frozen within 24 hours after phlebotomy. CONCLUSIONS: Almost 3 years after the publication of the initial AABB bulletin on this issue, TRALI risk reduction strategies are commonly employed at most US blood centers. However, procedures are not uniform.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".