Report on the 19th International Society of Blood Transfusion Platelet Immunology Workshop 2018
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aims of the 19th International Society of Blood Transfusion Platelet Immunology Workshop were to compare the sensitivity and specificity of in-house and commercially available methods for the detection of alloantibodies against human platelet antigens. Survey regarding laboratory management of samples collected for the diagnosis of foetal neonatal alloimmune thrombocytopenia was also conducted. MATERIALS AND METHODS: Twenty-nine laboratories from 17 countries were invited to participate. Seven serum or plasma samples for antibody identification and eight DNA samples for genotyping were sent to participating laboratories. Additionally, samples, critical reagents, materials and instructions for three exercises, one using a commercial kit (Pak Lx), one on platelet preparation for the detection of anti-HPA-3 antibodies and one for testing four anti-CD109 monoclonal antibodies for anti-HPA-15 antibody detection, were provided. RESULTS: Anti-HPA-1a, anti-HPA-2b, anti-HPA-5b and anti-GPIV were detected by the majority of the 28 reporting laboratories using their respective in-house MAIPA assay and/or a commercially available assay. Conversely, very few laboratories correctly identified anti-HPA-3a and HPA-15b. DNA genotyping of HPA and HLA alleles was highly accurate, with just a few discrepancies relative to the expected results. The Pak Lx kit has proven reliable for detecting anti-HPA-1a, anti-HPA-5a and anti-HLA; however, it failed at identifying an anti-HPA-3a in a clinical sample. CONCLUSIONS: Some anti-platelet alloantibodies are reliably and consistently detected, yet others remain difficult to detect. Genotyping of HPA and HLA alleles has proven to be highly accurate and robust. Future work should focus on optimizing the detection of anti-HPA-3 and anti-HPA-15 antibodies.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.113 | 0.046 |
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".