Guidance for the procurement of COVID‐19 convalescent plasma: differences between high‐ and low‐middle‐income countries
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: COVID-19 convalescent plasma (CCP) has been used, predominantly in high-income countries (HICs) to treat COVID-19; available data suggest the safety and efficacy of use. We sought to develop guidance for procurement and use of CCP, particularly in low- and middle-income countries (LMICs) for which data are lacking. MATERIALS AND METHODS: A multidisciplinary, geographically representative group of individuals with expertise spanning transfusion medicine, infectious diseases and haematology was tasked with the development of a guidance document for CCP, drawing on expert opinion, survey of group members and review of available evidence. Three subgroups (i.e. donor, product and patient) were established based on self-identified expertise and interest. Here, the donor and product-related challenges are summarized and contrasted between HICs and LMICs with a view to guide related practices. RESULTS: The challenges to advance CCP therapy are different between HICs and LMICs. Early challenges in HICs related to recruitment and qualification of sufficient donors to meet the growing demand. Antibody testing also posed a specific obstacle given lack of standardization, variable performance of the assays in use and uncertain interpretation of results. In LMICs, an extant transfusion deficit, suboptimal models of donor recruitment (e.g. reliance on replacement and paid donors), limited laboratory capacity for pre-donation qualification and operational considerations could impede wide adoption. CONCLUSION: There has been wide-scale adoption of CCP in many HICs, which could increase if clinical trials show efficacy of use. By contrast, LMICs, having received little attention, require locally applicable strategies for adoption of CCP.
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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 | 0.044 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".