Building the foundation for a community-generated national research blueprint for inherited bleeding disorders: research priorities for ultra-rare inherited bleeding disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it