The future of blood services amid a tight balance between the supply and demand of blood products: Perspectives from the <scp>ISBT</scp> Young Professional Council
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Blood services manage the increasingly tight balance between the supply and demand of blood products, and their role in health research is expanding. This review explores the themes that may define the future of blood banking. MATERIALS AND METHODS: We reviewed the PubMed database for articles on emerging/new blood-derived products and the utilization of blood donors in health research. RESULTS: In high-income countries (HICs), blood services may consider offering these products: whole blood, cold-stored platelets, synthetic blood components, convalescent plasma, lyophilized plasma and cryopreserved/lyophilized platelets. Many low- and middle-income countries (LMICs) aim to establish a pool of volunteer, non-remunerated blood donors and wean themselves off family replacement donors; and many HICs are relaxing the deferral criteria targeting racial and sexual minorities. Blood services in HICs could achieve plasma self-sufficiency by building plasma-dedicated centres, in collaboration with the private sector. Lastly, blood services should expand their involvement in health research by establishing donor cohorts, conducting serosurveys, studying non-infectious diseases and participating in clinical trials. CONCLUSION: This article provides a vision of the future for blood services. The introduction of some of these changes will be slower in LMICs, where addressing key operational challenges will likely be prioritized.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".