Human neutrophil antibodies in a blood donor population: a lookback study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Human neutrophil antibodies (HNA) have been associated with severe transfusion-related acute lung injury (TRALI). We identified HNA antibodies in a blood donor population and performed an observational lookback on patients who received products from these donors to determine whether TRALI was associated with these transfusions. MATERIALS AND METHODS: Human neutrophil antibodies were determined in 1171 blood donors (388 non-transfused males, 390 human leucocyte antigen (HLA) antibody-negative females and 393 HLA antibody-positive females) for IgG and IgM antibodies using a flow cytometric assay. Selected positive samples had a monoclonal antibody immobilization of granulocyte antigen (MAIGA) and neutrophil genotyping performed to confirm specificity. Lookback was performed on patients receiving blood from donors with positive samples by extracting recipient data from hospital medical records. An expert panel of three pulmonary critical care physicians reviewed the summarized data and assigned a diagnosis of TRALI, possible TRALI, cannot distinguish between TRALI and TACO, TACO and other. RESULTS: Eight donors had HNA antibodies of which five contributed to this lookback (3-HNA-specific antibodies, 2-HNA non-specific antibodies). Seventy-six blood products were transfused from these donors into individual patients. One patient developed TRALI that was associated with a donor with a non-specific HNA antibody as well as class-I and class-II HLA antibodies. CONCLUSION: The incidence of TRALI in this lookback was low and combined with low frequency of HNA antibodies in the donor population suggests not screening donors for HNA antibodies at this time is acceptable.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".