Building the foundation for a community-generated national research blueprint for inherited bleeding disorders: research priorities for ultra-rare inherited bleeding disorders
Bibliographic record
Abstract
BACKGROUND: Ultra-rare inherited bleeding disorders (BDs) present important challenges for generating a strong evidence foundation for optimal diagnosis and management. Without disorder-appropriate treatment, affected individuals potentially face life-threatening bleeding, delayed diagnosis, suboptimal management of invasive procedures, psychosocial distress, pain, and decreased quality-of-life. RESEARCH DESIGN AND METHODS: The National Hemophilia Foundation (NHF) and the American Thrombosis and Hemostasis Network identified the priorities of people with inherited BDs and their caregivers, through extensive inclusive community consultations, to inform a blueprint for future decades of research. Multidisciplinary expert Working Group (WG) 3 distilled highly feasible transformative ultra-rare inherited BD research opportunities from the community-identified priorities. RESULTS: WG3 identified three focus areas with the potential to advance the needs of all people with ultra-rare inherited BDs and scored the feasibility, impact, and risk of priority initiatives, including 13 in systems biology and mechanistic science; 2 in clinical research, data collection, and research infrastructure; and 5 in the regulatory process for novel therapeutics and required data collection. CONCLUSIONS: Centralization and expansion of expertise and resources, flexible innovative research and regulatory approaches, and inclusion of all people with ultra-rare inherited BDs and their health care professionals will be essential to capitalize on the opportunities outlined herein.
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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 | 0.158 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.031 | 0.041 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".