International Society of Blood Transfusion survey of experiences of blood banks and transfusion services during the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has impacted blood systems worldwide. Challenges included maintaining blood supplies and initiating the collection and use of COVID-19 convalescent plasma (CCP). Sharing information on the challenges can help improve blood collection and utilization. MATERIALS AND METHODS: A survey questionnaire was distributed to International Society of Blood Transfusion members in 95 countries. We recorded respondents' demographic information, impacts on the blood supply, CCP collection and use, transfusion demands and operational challenges. RESULTS: Eighty-two responses from 42 countries, including 24 low- and middle-income countries, were analysed. Participants worked in national (26.8%) and regional (26.8%) blood establishments and hospital-based (42.7%) institutions. CCP collection and transfusion were reported by 63% and 36.6% of respondents, respectively. Decreases in blood donations occurred in 70.6% of collecting facilities. Despite safety measures and recruitment strategies, donor fear and refusal of institutions to host blood drives were major contributing factors. Almost half of respondents working at transfusion medicine services were from large hospitals with over 10,000 red cell transfusions per year, and 76.8% of those hospitals experienced blood shortages. Practices varied in accepting donors for blood or CCP donations after a history of COVID-19 infection, CCP transfusion, or vaccination. Operational challenges included loss of staff, increased workloads and delays in reagent supplies. Almost half of the institutions modified their disaster plans during the pandemic. CONCLUSION: The challenges faced by blood systems during the COVID-19 pandemic highlight the need for guidance, harmonization, and strengthening of the preparedness and the capacity of blood systems against future infectious threats.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".