Contrasting Approaches in the Implementation of GRADE Methodology in Guidelines for Haemophilia and Von Willebrand Disease
Bibliographic record
Abstract
INTRODUCTION: The 2024 ISTH clinical practice guideline (CPG) for treatment of congenital haemophilia, the NBDF-McMaster Guideline on Care Models for Haemophilia Management, and ASH ISTH NBDF WFH guidelines on the diagnosis and management of VWD all utilised GRADE methodology. AIM: Discuss missed opportunities and the methodological approach of the ISTH Guideline in contrast to how GRADE was previously applied in rare diseases. METHODS: Critically analyse the methodology of each guideline along with best practices in the use of GRADE. Where applicable, the WFH Guidelines for the Management of Haemophilia were analysed. RESULTS: Important differentiating features in applying GRADE were identified. Where a strong evidence base is lacking, data other than those from randomized controlled trials, which may not always be justified, need to be considered, including incorporation of outcomes important to people living with the disease. Justification and stakeholder input to prioritize questions requiring a new guideline, panel composition with necessary patient participation and content expertise were also found to be significant differentiating features. CONCLUSION: The puristic approach taken in the ISTH Guideline development process, without consideration of accepted adaptations to GRADE implementation, created a missed opportunity for progressing haemophilia care, leading to guideline recommendations that have been widely deemed invalid and obsolete by expert healthcare professionals and by those living with the condition, the very people who are expected to implement or bear the impact of the recommendations. Lessons learnt from this comparative analysis should guide future guideline development and encourage collaboration to further advance haemophilia.
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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 | 0.723 | 0.898 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".