Blood donor eligibility criteria for medical conditions: A <scp>BEST</scp> collaborative study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Donor eligibility questions and criteria for medical conditions vary between blood centres, suggesting that they are based more on local regulations or experience, rather than on published data, which are limited. As the donor population ages, medical conditions become more common. We assessed donor health assessment criteria at blood centre members of the Biomedical Excellence for Safer Transfusion (BEST) Collaborative. Our aim was to compare eligibility criteria and determine their underlying basis. MATERIALS AND METHODS: A REDCap survey was sent to blood centre participants, based on medical conditions of greatest interest suggested by the Donor Studies Team of the BEST Collaborative. Participants were asked about current donor health assessment questions, deferral criteria and the basis for their deferral policy (donor risk, recipient risk or both) for 20 medical conditions. RESULTS: Complete responses were received from 26 blood donor centres (24 separate responses) representing a combination of hospital-based centres, large regional centres and community/national blood centres in 14 different countries. Most centres specifically ask about heart and lung conditions, whereas fewer than half inquire about kidney, gastrointestinal or neurological conditions. North American blood centres tended to be less restrictive, while regulatory restrictions are more prevalent in Europe. Most participants felt that the criteria were based on regulatory requirements or experience, rather than on published data. CONCLUSION: There is considerable variability in criteria by region. Ideally, criteria would be more evidence-based rather than based on regulatory requirements or experience. Deferral criteria must balance donor and recipient safety and maintain an adequate blood supply.
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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 | 0.039 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".