Characterization of copy-number variants in a large cohort of patients with von Willebrand disease reveals a relationship between disrupted regions and disease type
Bibliographic record
Abstract
Background: ) gene; however, this technique cannot always detect copy-number variants (CNVs). Additional mapping of CNVs in patients with VWD is needed. Objectives: This study aimed to characterize CNVs in a large sample of VWF mutation-negative VWD patients. Methods: pseudogene. This was performed on 204 mutation-negative subjects for whom clinical variables were also available. Results: Among the 204 patients, 7 unique CNVs were found, with a total of 24 CNVs (12%). Of the 7 unique CNVs, 1 was novel, 1 was found in a VWF database, and 5 were previously reported. All patients with type 1C VWD and a CNV had the same exon 33 and 34 in-frame deletion. Certain clinical variables were also significantly different between those with and without CNVs. Conclusion: The in-frame deletion in patients with type 1C VWD exactly matches the D4N module of the D4 domain, a region where mutations and deletions are known to affect clearance. We observed significantly higher VWF-to-ristocetin cofactor levels in patients with type 1C VWD and a CNV than in patients without a CNV, suggesting a relationship between CNVs and the increased clearance observed in patients with type 1C VWD. Glycoprotein IbM activity was significantly lower in patients with type 1 VWD and a CNV than in patients without a CNV, suggesting that platelet binding is more affected by CNVs than single base pair mutations. This work elucidates some of the underlying genetic mechanisms of CNVs in 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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".