Type‐3 von Willebrand disease in India—Clinical spectrum and molecular profile
Bibliographic record
Abstract
INTRODUCTION: Type 3 von Willebrand disease (VWD) is the rare and most severe form of VWD which results from a near-complete deficiency of the von Willebrand factor (VWF). This study evaluates in detail the molecular pathology of type-3 VWD in India. One hundred and two patients from 90 families were evaluated. PATIENTS AND METHODS: Phenotypic data, including bleeding scores (BS), were documented using structured questionnaires. Diagnosis of type 3 VWD was based on undetectable VWF antigen levels in the plasma. Genomic DNA from these patients was screened for mutations in VWF gene. Structural modeling and expression studies were carried out for missense mutations. RESULTS: Out of 102 patients, mutations could be identified in 91% (n = 93). Fifty-five different gene variants were identified. Thirty-four (61.8%) were novel. Mutations could be identified in both the alleles in 90 patients, while no causative mutation could be identified in 9 patients; twenty-four (23.5%) patients had mutations clustered in the propeptide region of VWF. Interestingly, five mutations accounted for the defects in 37/93 (39.8%) patients. Structural analysis and in vitro studies on missense mutations imply impaired processes associated with secretion of VWF. CONCLUSION: This study is one of the largest series to define the molecular basis of type-3 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".