Clinical use of Convalescent Plasma in the COVID‐19 pandemic: a transfusion‐focussed gap analysis with recommendations for future research priorities
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Use of convalescent plasma for coronavirus disease 2019 (COVID-19) treatment has gained interest worldwide. However, there is lack of evidence on its dosing, safety and effectiveness. Until data from clinical studies are available to provide solid evidence for worldwide applicable guidelines, there is a need to provide guidance to the transfusion community and researchers on this emergent therapeutic option. This paper aims to identify existing key gaps in current knowledge in the clinical application of COVID-19 convalescent plasma (CCP). MATERIALS AND METHODS: The International Society of Blood Transfusion (ISBT) initiated a multidisciplinary working group with worldwide representation from all six continents with the aim of reviewing existing practices on CCP use from donor, product and patient perspectives. A subgroup of clinical transfusion professionals was formed to draft a document for CCP clinical application to identify the gaps in knowledge in existing literature. RESULTS: Gaps in knowledge were identified in the following main domains: study design, patient eligibility, CCP dose, frequency and timing of CCP administration, parameters to assess response to CCP treatment and long-term outcome, adverse events and CCP application in less-resourced countries as well as in paediatrics and neonates. CONCLUSION: This paper outlines a framework of gaps in the knowledge of clinical deployment of CPP that were identified as being most relevant. Studies to address the identified gaps are required to provide better evidence on the effectiveness and safety of CCP use.
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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 | 0.078 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 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".