Frequency of Platelet type versus Type 2B von Willebrand Disease
Bibliographic record
Abstract
Less than 50 patients are reported with platelet type von Willebrand disease (PT-VWD) worldwide. Several reports have discussed the diagnostic challenge of this disease versus the closely similar disorder type 2B VWD. However, no systematic study has evaluated this dilemma globally. Over three years, a total of 110 samples/data from eight countries were analysed. A molecular approach was utilised, analysing exon 28 of the von Willebrand factor (VWF) gene, and in mutation negative cases the platelet GP1BA gene. Our results show that 48 cases initially diagnosed as putative type 2B/PT-VWD carried exon 28 mutations consistent with type 2B VWD, 17 carried GP1BA mutations consistent with a PT-VWD diagnosis, three had other VWD types (2A and 2M) and five expressed three non-previously published exon 28 mutations. Excluding 10 unaffected family members and one acquired VWD, 26 cases did not have mutations in either genes. Based on our study, the percentage of type 2B VWD diagnosis is 44% while the percentage of misdiagnosis of PT-VWD is 15%. This is the first large international study to investigate the occurrence of PT-VWD and type 2B VWD worldwide and to evaluate DNA analysis as a diagnostic tool for a large cohort of patients. The study highlights the diagnostic limitations due to unavailability/poor application of RIPA and related tests in some centres and proposes genetic analysis as a suitable tool for the discrimination of the two disorders worldwide. Cases that are negative for both VWF and GP1BA gene mutations require further evaluation for alternative diagnoses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".