Diagnosis of von Willebrand disease: An assessment of the quality of testing in North American laboratories
Bibliographic record
Abstract
BACKGROUND: Laboratory diagnosis of von Willebrand Disease (VWD) is complex. Reliance on laboratory testing can be problematic as different VWD screening panels, assays and methodologies can produce analytic variability in test results. OBJECTIVES: To compare the degree of imprecision among the VWD assays and within the platelet binding activity (PBA) assays, to determine the consensus among the VWD assays for correct classification of sample results, and to determine consensus among laboratories' von Willebrand factor (VWF) multimer interpretations and final interpretations of the VWD panels. PATIENTS/METHODS: Proficiency testing results from the North American Specialized Coagulation Laboratory Association (NASCOLA) submitted by laboratories from 2010 to 2019 for all normal, type (T) 1 VWD and T2 VWD samples were analysed. RESULTS AND CONCLUSIONS: Imprecision was lowest for VWF antigen and highest for collagen binding activity (CBA) with median coefficient of variation (CV) of 12% (interquartile range (IQR) 7%) and 23% (IQR 21%) respectively. Within the VWF PBA assays, the gain-of-function mutant GP1b binding (VWF: GP1bM) methods had the least imprecision (CV 9%, IQR 10%). All assays, including the various PBA methods had excellent consensus. The majority of laboratories agreed that normal (median consensus-82%, IQR 16%) and T1 VWD (median consensus-100%, IQR 9%) samples had normal multimer distribution. Consensus among laboratories for final interpretations was excellent for normal samples (median 81%, IQR 8%), good for T1 VWD samples (median 59%, IQR 9%), and fair for T2 VWD samples (median 44%, IQR 21%). Consensus on final interpretation decreased as sample complexity increased.
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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 | 0.054 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".