von Willebrand factor levels in the diagnosis of von Willebrand disease: a systematic review and meta-analysis
Bibliographic record
Abstract
von Willebrand disease (VWD) is associated with significant morbidity as a result of excessive mucocutaneous bleeding. Early diagnosis and treatment are important to prevent and treat these symptoms. We systematically reviewed the accuracy of diagnostic tests using different cutoff values of von Willebrand factor antigen (VWF:Ag) and platelet-dependent von Willebrand factor (VWF) activity assays in the diagnosis of VWD. We searched Cochrane Central Register for Controlled Trials, MEDLINE, and Embase databases for eligible studies. We pooled estimates of sensitivity and specificity and reported patient-important outcomes when relevant. This review included 21 studies that evaluated VWD diagnosis. The results showed low certainty in the evidence for a net health benefit from reconsidering the diagnosis of VWD vs removing the disease diagnosis in patients with VWF levels that have normalized with age. For the diagnosis of type 1 VWD, VWF sequence variants were detected in 75% to 82% of patients with VWF:Ag < 0.30 IU/mL and in 44% to 60% of patients with VWF:Ag between 0.30 and 0.50 IU/mL. A sensitivity of 0.90 (95% confidence interval [CI], 0.83-0.94) and a specificity of 0.91 (95% CI, 0.76-0.97) were observed for a platelet-dependent VWF activity/VWF:Ag ratio < 0.7 in detecting type 2 VWD (moderate certainty in the test accuracy results). VWF:Ag and platelet-dependent activity are continuous variables that are associated with an increase in bleeding risk with decreasing levels. This systematic review shows that using a VWF activity/VWF:Ag ratio < 0.7 vs lower cutoff levels in patients with an abnormal initial VWD screen is more accurate for the diagnosis of type 2 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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".