Utility of a Paediatric Bleeding Questionnaire as a screening tool for von Willebrand disease in apparently healthy children
Bibliographic record
Abstract
UNLABELLED: von Willebrand disease (VWD), an inherited bleeding disorder caused by deficiency or dysfunction of von Willebrand factor (VWF) is diagnosed when a personal and often a family history of excessive mucocutaneous bleeding is present along with abnormal laboratory studies. An accurate assessment of haemorrhagic symptoms is key in suspecting VWD but presents a challenge especially in children due to overlap between normal and abnormal bleeding. Bleeding questionnaire (BQ) scores have been validated in adults and have recently been validated in children with VWD for assessing bleeding severity. However, there are limited data supporting their use prospectively in healthy children with bleeding complaints. AIM: The objectives of this study were to obtain normative data from children and validate a paediatric BQ (PBQ) to determine the discriminative ability of its total score and its individual components for identifying children likely to have VWD. METHODS: The PBQ was administered to 1281 multiethnic, healthy children between 30 days and 18 years of age presenting to a general paediatric office and to 35 children with VWD based on VWF antigen, activity and multimer pattern. RESULTS: When children with total BQ scores of 3 or more were predicted to have VWD, the sensitivity was 97.2%, the specificity was 97.1%, the positive predictive value was 48.6% and the negative predictive value was 99.9%. CONCLUSIONS: The PBQ may help discriminate a significant bleeding history from trivial bleeding, may be integrated into the primary care algorithm for evaluating children suspected with 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".