Generation and optimization of the self‐administered bleeding assessment tool and its validation as a screening test for von Willebrand disease
Bibliographic record
Abstract
INTRODUCTION/AIM: Our aim was to generate, optimize and validate a self-administered bleeding assessment tool (self-BAT) for von Willebrand disease (VWD). METHODS: In Phase 1, medical terminology in the expert-administered International Society on Thrombosis and Haemostasis (ISTH)-BAT was converted into a Grade 4 reading level to produce the first version of the Self-BAT which was then optimized to ensure agreement with the ISTH-BAT. In Phase 2, the normal range of bleeding scores (BSs) was determined and test-retest reliability analysed. In Phase 3, the optimized Self-BAT was tested as a screening tool for first time referrals to the Haematology clinic. RESULTS: Bleeding score from the final optimized version of the Self-BAT showed an excellent intra-class correlation coefficient (ICC) of 0.87 with ISTH-BAT BS in Phase 1. In Phase 2, the normal range of BSs for the optimized Self-BAT was determined to be 0 to +5 for females and 0 to +3 for males and excellent test-retest reliability was shown (ICC = 0.95). In Phase 3, we showed that a positive Self-BAT BS (≥6 for females, ≥4 for males) has a sensitivity of 78%, specificity of 23%, positive predictive value (PPV) of 0.15 and negative predictive value (NPV) of 0.86 for VWD; these figures improved when just the females were analysed; sensitivity of 100%, specificity of 21%, PPV = 0.17 and NPV = 1.0. CONCLUSION: We show an optimized Self-BAT can generate comparable BS to the expert-administered ISTH-BAT and is a reliable, effective screening tool to incorporate into the assessment of individuals, particularly women, referred for a possible bleeding disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| 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".