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 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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".