White paper on pandemic preparedness in the blood supply
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In March 2020, the WHO declared the SARS-CoV-2 corona virus a pandemic which caused a great disruption to global society and had a pronounced effect on the worldwide supply of blood. MATERIALS AND METHODS: In 2022 an on-line meeting was organised with experts from Austria, Canada, Germany, Greece, Netherlands and United States to explore the opportunities for increasing preparedness within blood systems for a potential future pandemic with similar, or more devastating, consequences. The main themes included the value of preparedness, current risks to the blood supply, supply chain vulnerabilities, and the role of innovation in increasing resiliency and safety. RESULTS: Seven key recommendations were formulated and including required actions at different levels. CONCLUSION: Although SARS-CoV-2 might be seen as a unique event, global health risks are expected to increase and will affect blood transfusion medicine if no preparedness plans are developed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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 teacher head, 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".