Bleeding Scores for the Diagnosis of von Willebrand Disease
Bibliographic record
Abstract
Obtaining a personal history of bleeding is a critical component to the diagnosis of von Willebrand disease (VWD). The collection of this information can be challenging for physicians, however, as the reporting and interpretation of bleeding symptoms is subjective. The need for more precise quantification of bleeding symptoms was recognized and the Vicenza Bleeding Questionnaire was developed in 2005. This questionnaire collects data regarding the presence and severity of bleeding symptoms and generates a bleeding score by summing the severity of all symptoms reported by a patient. Several subsequent bleeding assessment tools (BATs) have been developed based from this original questionnaire and there has been a surge in the use of BATs in various clinical settings for the diagnosis and evaluation of VWD. This review will discuss the evolution of BATs over the past decade, as well as their use and validation in various settings for the diagnosis and evaluation of VWD. Additionally, we will discuss the clinical utility of BATs, the limitations of these tools, and future directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".