Diagnosis of transfusion-related acute lung injury: TRALI or not TRALI?
Bibliographic record
Abstract
TRALI is a challenging diagnosis for both the transfusion specialist and the clinician. A Canadian consensus panel has recently proposed guidelines to better define TRALI and its implications. The guidelines recommend classifying each suspected case in one of the following 3 categories: (1) "TRALI," (2) "Possible TRALI," or (3) "Not TRALI." We report the clinical presentation, laboratory evaluation, and management of 3 patients with respiratory failure (RF) following allogeneic blood transfusions. These patients all experienced RF within 6 hr post-transfusion. Based on a review of the clinical and laboratory data and applying the Canadian guidelines, the first patient, a 67-yr-old man with chronic myelomonocytic leukemia, was diagnosed as "TRALI" due to the sudden onset of RF requiring intensive resuscitation. The second patient, a 55-yr-old man with aplastic anemia, was diagnosed as "Possible TRALI" due to pre-existing RF that worsened after blood transfusion. The third patient, a 1-yr-old male, was diagnosed as transfusion associated circulatory overload (TACO) and "Possible TRALI," although his RF improved after treatment with diuretics. In all 3 cases, the blood donor center was informed of the suspected TRALI reactions. The remaining blood products from the donors associated with these reactions were quarantined. After review of the clinical data, the donors associated with cases #1 and #3 were screened by the blood center for granulocyte and HLA antibodies. Using a Luminex flow bead array, the following class I and class II antibodies specific for patient #1 were identified in the respective donor: anti-A25, B8, B18, and anti-DR15, DR 17. Subsequently, donor #1 was permanently deferred. A non-specific IgM anti-granulocyte antibody was identified in the donor associated with case #3, and this donor was subsequently disqualified from plasma and platelet donations. In conclusion, the Canadian guidelines to categorize patients suspected of TRALI provide a useful framework for evaluation of these patients and their respective blood donors.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".