Patient‐reported data on the severity of Von Willebrand disease
Bibliographic record
Abstract
INTRODUCTION: The severity of Von Willebrand disease (VWD) is currently based on laboratory phenotype. However, little is known about the severity of the patient's experience with the disease. The most recent VWD guidelines highlight the need for patient-reported outcomes (PROs) in VWD. AIM: The study aimed to investigate the patient-perspective on VWD severity and to identify key factors that determine the severity of disease experienced by patients. MATERIALS AND METHODS: Patients participated in a nationwide cross-sectional study on VWD in the Netherlands (WiN-study). Patients filled in a questionnaire containing questions on the experienced severity of VWD (4-point scale), bleeding score (BS) and quality of life (QoL). RESULTS: We included 736 patients, median age of 41.0 years (IQR 23.0-55.0) and 59.5% were women. A total of 443 had type 1, 269 type 2 and 24 type 3 VWD. Self-reported severity of VWD was categorized as severe (n = 52), moderate (n = 171), mild (n = 393) or negligible (n = 120). Classification by historically lowest FVIII:C levels < 0.20 IU/mL as a proxy for severe VWD aligned with patient-reported severity classification with a 72% accuracy. Type 3 VWD (OR = 4.02, 95%CI: 1.72-9.45), higher BS (OR = 1.09, 95%CI: 1.06-1.11), female sex (OR = 1.36, 95%CI: 1.01-1.83), haemostatic treatment in the year preceding study inclusion (OR = 1.53, 95%CI: 1.10-2.13) and historically lowest VWF:Act levels (OR = 0.26, 95%CI: 0.07-1.00) were independent determinants of patient-reported severity. CONCLUSION: This study shows that patient-reported data provide novel insights into the determinants of experienced disease severity. Our findings highlight the need for studies on PROs with validated questionnaires to assess the burden of 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 | 0.002 | 0.007 |
| 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) | 0.004 | 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".