Sixth Åland Island Conference on von Willebrand disease
Bibliographic record
Abstract
INTRODUCTION: The sixth Åland Islands Conference on von Willebrand disease (VWD) on the Åland Islands, Finland, was held from 20 to 22 September 2018. AIM: The meeting brought together experts in the field of VWD from around the world to share the latest advances and knowledge in VWD. RESULTS AND DISCUSSION: The topics covered both clinical aspects of disease management, and biochemical and laboratory insights into the disease. The clinical topics discussed included epidemiology, diagnosis and treatment of VWD in different countries, management of children with VWD, bleeding control during surgery, specific considerations for the management of type 3 VWD and bleeding control in women with VWD. Current approaches to the management of acquired von Willebrand syndrome were also discussed. Despite significant advances in the understanding and therapeutic options for VWD, there remain many challenges to be overcome in order to optimise patient care. In comparison with haemophilia A, there are very few registries of VWD patients, which would be a valuable source of data on the condition and its management. VWD is still underdiagnosed, and many patients suffer recurrent or severe bleeding that could be prevented. Awareness of VWD among healthcare practitioners, including non-haematologists, should be improved to allow timely diagnosis and intervention. Diagnosis remains challenging, and the development of fast, simple assays may help to facilitate accurate and rapid diagnosis of 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".