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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".