AABB survey of transfusion‐related acute lung injury policies and practices in the United States
Bibliographic record
Abstract
BACKGROUND: Policies and practices with regard to transfusion-related acute lung injury (TRALI) diagnosis, laboratory investigation of TRALI cases, and donor deferral and donor management are not standardized. STUDY DESIGN AND METHODS: A Web-based survey was designed and administered to participating AABB member institutions in July 2006. RESULTS: The survey response rate was highest for donor centers, followed by larger hospital blood banks and transfusion services. Laboratory case workups regularly included HLA Class I and II antibody testing of donors followed less frequently by HNA antibody testing; recipient specimens for leukocyte antigen typing were usually not obtained, even if indicated as part of the planned workup. Several different criteria (i.e., all donors, female donors only, case by case determination) were used to select which donors should be tested. There was agreement that donors should be deferred if implicated in a TRALI case (i.e., antibody-cognate antigen match); however, donor management policies varied in other scenarios. The final diagnosis of TRALI was often (45%-66% depending on institutional type) based on a combination of clinical and serologic findings rather than on adherence to the clinical definition recommended by the Canadian Consensus Conference. Many TRALI policies appeared to be decided on a case-by-case basis at the discretion of the institution's medical director. CONCLUSIONS: There is wide variability in procedures and policies related to the diagnosis of and donor investigation and/or management of TRALI cases. Lack of a consensus approach may partly reflect limitations in understanding of TRALI pathogenesis. The survey suggests that increased education of transfusion medicine practitioners is needed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".