A core outcome set for prophylaxis and perioperative treatment of von Willebrand disease: The coreVWD initiative
Bibliographic record
Abstract
INTRODUCTION: Treatment options are expanding for von Willebrand disease (VWD). A core outcome set (COS)-a minimum set of agreed-upon outcomes to be used in every clinical trial for a given condition-provides guidance on which outcomes are most important to measure to ensure necessary data is collected for a variety of stakeholders and enable comparison across products and trials. AIM: coreVWD aimed to develop a COS for trials for prophylaxis and perioperative treatments for VWD. METHODS: A modified Delphi consensus process was used to condense/prioritize a long list of potential outcomes. Over three Delphi rounds, a multi-stakeholder panel (patients, clinicians, pharmaceutical company representatives, HTA organizations, payer, and government organization representatives) rated each outcome from 1 (not important to include in a COS) to 9 (essential to include). Outcomes were eliminated or retained based on pre-determined criteria; a special provision to elevate patient priorities was included. An in-person consensus meeting was held after Delphi round 2. RESULTS: Thirty-nine panellists participated. The final COS for prophylaxis treatment included 18 outcomes, seven of which are part of a special subset selected for women, girls and people with the potential to menstruate. There were 11 outcomes in the final perioperative branch COS. Six outcomes overlapped both COS. CONCLUSIONS: The coreVWD COS represents a consensus list of outcomes for clinical trials for both factor and non-factor VWD therapies. These outcomes will be useful across the lifecycle of a product, from clinical development through regulatory and market access phases and into patient-provider decision-making.
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 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 | 0.249 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".