Scanning the Future of Transfusion Medicine
Bibliographic record
Abstract
Transfusion medicine is a technology-based discipline undergoing continuous change. We summarize recent significant changes and likely future changes to blood collection and component processing, hospital-based transfusion medicine and cellular therapies. Automation, standardization and a focus on quality and safety will continue to characterize blood component production. Although pathogen reduction technology remains an area of key interest, blood component safety initiatives will require a perspective grounded in cost effectiveness and informed by risk-based decision making. Increased data on clinical transfusion decisions will allow haemovigilance to improve patient outcomes. Noninvasive devices that measure tissue oxygenation will improve clinical decision-making for red cell transfusion. New oral and intravenous anticoagulants whose effect is reversible and antigen-specific immune suppression would represent substantial therapeutic advances. Haemopoietic stem cell transplantation will need to minimize the toxicities of graft-versus-host disease. Although other cellular therapies will explore immunotherapy against cancer and nonmalignant disorders, the extreme cost of these treatments may limit their use. The 21st century is witnessing an unprecedented disparity in wealth distribution throughout the world. Transfusion medicine and all disciplines of medicine will face difficult choices between increasing healthcare technology or increasing worldwide health.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".