Building the foundation for a community-generated national research blueprint for inherited bleeding disorders: facilitating research through infrastructure, workforce, resources and funding
Bibliographic record
Abstract
BACKGROUND: The National Hemophilia Foundation (NHF) conducted extensive, inclusive community consultations to guide prioritization of research in coming decades in alignment with its mission to find cures and address and prevent complications enabling people and families with blood disorders to thrive. RESEARCH DESIGN AND METHODS: With the American Thrombosis and Hemostasis Network, NHF recruited multidisciplinary expert working groups (WG) to distill the community-identified priorities into concrete research questions and score their feasibility, impact, and risk. WG6 was charged with identifying the infrastructure, workforce development, and funding and resources to facilitate the prioritized research. Community input on conclusions was gathered at the NHF State of the Science Research Summit. RESULTS: WG6 detailed a minimal research capacity infrastructure threshold, and opportunities to enable its attainment, for bleeding disorders centers to participate in prospective, multicenter national registries. They identified challenges and opportunities to recruit, retain, and train the diverse multidisciplinary care and research workforce required into the future. Innovative collaborative approaches to trial design, resource networking, and funding to surmount obstacles facing research in rare disorders were elucidated. CONCLUSIONS: The innovations in infrastructure, workforce development, and resources and funding proposed herein may contribute to facilitating a National Research Blueprint for Inherited Bleeding Disorders.
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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 | 0.216 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.010 | 0.055 |
| Research integrity | 0.027 | 0.033 |
| Insufficient payload (model declined to judge) | 0.024 | 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; 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".