Bleeding assessment tools to predict von Willebrand disease: Utility of individual bleeding symptoms
Bibliographic record
Abstract
BACKGROUND: Bleeding assessment is part of the diagnostic workup of von Willebrand disease (VWD). Bleeding assessment tools (BATs) have standardized obtaining this information but have been criticized because they are time consuming. OBJECTIVE: To use our legacy data to determine which questions from BATs are the strongest predictors of a VWD diagnosis. PATIENTS/METHODS: Bleeding score data from 3 different BATs were used. Patients aged <12 years were excluded. Questions on BATs relate to different bleeding symptoms, and each symptom is scored by severity. Scores for each symptom were sorted based on whether they indicated clinically significant bleeding, and nonsignificant scores were set as the reference category. Multivariable logistic regression was used to determine the symptoms that were the strongest predictors of a laboratory-confirmed VWD diagnosis. RESULTS: A total of 927 participants were included; 144 (16%) were patients with VWD, and 783 (84%) were healthy controls. The top 3 symptoms for which a clinically significant positive response increased the likelihood of VWD were hemarthrosis (odds ratio [OR], 19.2; 95% confidence interval [CI], 3.7-100.4), postsurgical bleeding (OR, 15.2; 95% CI, 5.9-38.9), and menorrhagia (OR, 10.3; 95% CI, 4.9-21.9). With each increase in number of bleeding symptom categories with clinically significant scores, subjects had a stepwise increase in odds of a VWD diagnosis. CONCLUSIONS: Our results suggest that most of the bleeding symptoms on BATs are significant predictors of VWD, and there is value in assessing multiple bleeding symptoms when eliciting a bleeding history. Certain bleeding symptoms are more useful predictors than others. Future BAT revisions may consider adding a relative weighting to each symptom.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".