Proceedings of the 2022 <scp>NHLBI</scp> and <scp>OASH</scp> state of the science in transfusion medicine symposium
Bibliographic record
Abstract
BACKGROUND: State of the Science (SoS) meetings are used to define and highlight important unanswered scientific questions. The National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health, and the Office of the Assistant Secretary for Health (OASH), Department of Health and Human Services held a virtual SoS in transfusion medicine (TM) symposium. STUDY DESIGN AND METHODS: In advance of the symposium, six multidisciplinary working groups (WG) convened to define research priorities in the areas of: blood donors and the supply, optimizing transfusion outcomes for recipients, emerging infections, mechanistic aspects of components and transfusion, new computational methods in transfusion science, and impact of health disparities on donors and recipients. The overall objective was to identify key basic, translational, and clinical research questions that will help to increase and diversify the volunteer donor pool, ensure safe and effective transfusion strategies for recipients, and identify which blood products from which donors best meet the clinical needs of specific recipient populations. RESULTS: On August 29-30, 2022, over 400 researchers, clinicians, industry experts, government officials, community members, and patient advocates discussed the research priorities presented by each WG. Dialogue focused on the five highest priority research areas identified by each WG and included the rationale, proposed methodological approaches, feasibility, and barriers for success. DISCUSSION: This report summarizes the key ideas and research priorities identified during the NHLBI/OASH SoS in TM symposium. The report highlights major gaps in our current knowledge and provides a road map for TM research.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.019 |
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".