Lessons learned in the collection of convalescent plasma during the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: The lack of definitive treatment or preventative options for COVID-19 led many clinicians early on to consider convalescent plasma (CCP) as potentially therapeutic. Regulators, blood centres and hospitals worldwide worked quickly to get CCP to the bedside. Although response was admirable, several areas have been identified to help improve future pandemic management. MATERIALS AND METHODS: A multidisciplinary, multinational subgroup from the ISBT Working Group on COVID-19 was tasked with drafting a manuscript that describes the lessons learned pertaining to procurement and administration of CCP, derived from a comprehensive questionnaire within the subgroup. RESULTS: While each country's responses and preparedness for the pandemic varied, there were shared challenges, spanning supply chain disruptions, staffing, impact of social distancing on the collection of regular blood and CCP products, and the availability of screening and confirmatory SARS-CoV-2 testing for donors and patients. The lack of a general framework to organize data gathering across clinical trials and the desire to provide a potentially life-saving therapeutic through compassionate use hampered the collection of much-needed safety and outcome data worldwide. Communication across all stakeholders was identified as being central to reducing confusion. CONCLUSION: The need for flexibility and adaptability remains paramount when dealing with a pandemic. As the world approaches the first anniversary of the COVID-19 pandemic with rising rates worldwide and over 115 million cases and 2·55 million deaths, respectively, it is important to reflect on how to better prepare for future pandemics as we continue to combat the current one.
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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 | 0.137 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".