Impact of Human Leucocyte Antigen epitope matched platelet transfusions in alloimmunised aplastic anaemia patients
Bibliographic record
Abstract
AIMS/OBJECTIVES: To explore the impact of Human Leucocyte Antigen (HLA)-A and B epitope-matched platelets on the outcome of platelet transfusions in alloimmunised patients with aplastic anaemia (AA). The relevance of HLA-C epitope mismatches was also investigated. BACKGROUND: Patients who become immunologically refractory (IR) to random platelet transfusions can experience an adequate rise in platelet count through the provision of HLA-compatible platelets using an antigen-matching algorithm. This approach has been shown to be effective in patients with a low calculated reaction frequency, but it is not always successful in highly sensitised patients. The use of HLA epitopes-selected platelets has been suggested as an alternative to the antigen matching approach. METHODS: The effect of HLA epitope matching (both Eplets and Triplets) on the outcome of platelet transfusion was analysed in 37 highly immunised AA patients previously transfused with HLA-A and B antigen-matched platelets. Epitope matching was determined using the HLAMatchmaker programme. The outcome of the transfusions was assessed by the platelet count increments (PCIs) obtained 1 and 24 hours post-transfusions. RESULTS: HLA-A and B epitope matching was equivalent to HLA antigen matching in raising platelet counts. There was no significant difference in PCI when HLA-C epitope mismatches were considered. In addition, transfusions with fewer than two antigen mismatches resulted in significantly higher PCIs compared to transfusions with more than two antigen mismatches. CONCLUSIONS: HLA epitope-matched platelet provision may represent a clinically effective transfusion strategy for patients IR to random platelet transfusions. Further prospective studies are required.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".