Development of the World Federation of Hemophilia Shared Decision‐Making Tool
Bibliographic record
Abstract
INTRODUCTION: The use of shared decision-making (SDM) in clinical settings is becoming more prevalent. The evolving and increasingly complex treatment landscape of haemophilia management has augmented the need and desire for SDM between patients and their healthcare team. SDM tools have been used in other chronic conditions and can be an effective form of education for patients and clinicians. AIM: The World Federation of Hemophilia (WFH) partnered with people with haemophilia (PWH), patient advocacy groups, and healthcare practitioners to form an expert working group to develop an educational tool for PWH and their caregivers. The primary objectives included educating PWH on the available prophylactic treatments and facilitating discussion between PWH and their healthcare team. METHODS: The tool was proposed and developed by the expert working group, workshopped at conference round tables, and evaluated in two focus groups. RESULTS: The interactive WFH SDM Tool guides users through the SDM treatment journey and provides an opportunity for reflection on current disease impact and treatment preferences, educational fact sheets and videos, and a comparison between treatment classes. Two forms of the SDM Tool are available: an online platform with a summary page that may be printed and shared and a printable workbook. All evidence in the tool is based on the prescribing information or phase III clinical trial publications. The Tool will be updated twice each year. CONCLUSION: The WFH SDM Tool is the first available resource that translates published guidance on SDM in haemophilia into a practical, user-friendly tool aimed at facilitating patient-centred treatment decisions.
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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 | 0.062 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".