Report on the Eleventh International Society of Blood Transfusion Platelet Genotyping and Serology Workshop
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aims of the Eleventh International Workshop were to evaluate proficiency in platelet genotyping and antibody detection, to equip laboratories to perform Gov antigen system genotyping and antibody detection, and to evaluate the laboratory and clinical approach to cases of neonatal alloimmune thrombocytopenia (NAIT). MATERIALS AND METHODS: There were 34 participating laboratories from 22 countries on five continents. Participating laboratories were provided with 10 DNA samples, 15 unknown sera, and three monoclonal antibodies for titration, as well as primer pairs and a protocol for Gov genotyping and Gov antibody screening. They were also provided with a questionnaire on investigation and clinical management of patients with NAIT. RESULTS: Thirty-three participants reported human platelet antigen (HPA)-1, -2, -3 and -5 genotyping results, 25 reported HPA-4 typing results, 17 reported HPA-6 typing results and 24 reported Gov typing results. For HPA-1-6 genotyping, 23 laboratories were concordant with a majority vote for all allotypes tested, five laboratories reported one deviation, three laboratories reported two deviations and one laboratory reported three deviations. For Gov genotyping, six deviations occurred in three of the 24 laboratories reporting results. Antibody detection was 90% concordant for anti-HPA-1a, anti-HPA-5a and anti-HPA-5b detection. Anti-HPA-2b and anti-Gova were detected by 20 and 14 out of 33 laboratories, respectively. Approaches to the clinical management of NAIT vary widely, especially for mothers with a history of a previous infant with mild NAIT. CONCLUSIONS: The overall error rate for HPA-1-6 genotyping decreased from 2.7% in the tenth workshop to 0.8% in the eleventh workshop. The majority of laboratories were able to perform Gov genotyping, although the error rate was 7.5%. Detection of common clinically significant antibodies was good, although detection of the much rarer HPA-2b was problematic. There was considerable progress in the detection of anti-Gova. The lack of consensus over treatment of NAIT demonstrates uncertainty over optimal management of these patients.
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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 | 0.010 | 0.004 |
| 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 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.017 |
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".