ADAMTS‐13 and bleeding phenotype in von Willebrand disease
Bibliographic record
Abstract
BACKGROUND: The bleeding phenotype of von Willebrand disease (VWD) varies highly between patients and can only partly be explained by von Willebrand factor (VWF) parameters. By cleaving large VWF multimers into smaller, less active multimers, ADAMTS-13 is an important regulator of VWF activity. However, it is unknown what the role of ADAMTS-13 is in individuals with VWD. OBJECTIVES: We therefore studied how ADAMTS-13 activity is associated with the laboratory and bleeding phenotype in individuals with VWD. METHODS: We measured ADAMTS-13 activity using the fluorescence resonance energy transfer substrate VWF 73 assay in 638 individuals with VWD in the nationwide cross-sectional Willebrand in the Netherlands study and in 36 healthy controls. The bleeding phenotype was assessed using the Tosetto bleeding score. RESULTS: ADAMTS-13 activity was similar in individuals with VWD (109% ± 20.6%) and controls (110% ± 19.7%). ADAMTS-13 activity was higher in individuals with VWD with type 3 than those with type 1 (mean difference, 11.8%; 95% confidence interval [CI], 2.9%-20.8%) or type 2 (mean difference, 16.1%; 95% CI, 7.1%-25.1%). ADAMTS-13 activity was not associated with the Tosetto bleeding score (0.1 Tosetto bleeding score increase per 10% ADAMTS-13 increase, 95% CI, -0.2 to 0.3). Furthermore, ADAMTS-13 activity did not differ between individuals with and without a bleeding event during the year preceding blood sampling (mean difference, 1.4%; 95% CI, -2.1% to 4.9%). CONCLUSION: ADAMTS-13 activity was highest in individuals with type 3 VWD, but it had only minor associations with VWF parameters. ADAMTS-13 activity does not influence the bleeding phenotype in individuals with VWD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".