Use of convalescent plasma in<scp>COVID</scp>‐19 patients with immunosuppression
Bibliographic record
Abstract
In the absence of effective countermeasures, human convalescent plasma has been widely used to treat severe acute respiratory syndrome coronavirus 2, the causative agent of novel coronavirus disease 19 (COVID-19), including among patients with innate or acquired immunosuppression. However, the association between COVID-19-associated mortality in patients with immunosuppression and therapeutic use of convalescent plasma is unknown. We review 75 reports, including one large matched-control registry study of 143 COVID-19 patients with hematological malignancies, and 51 case reports and 23 case series representing 238 COVID-19 patients with immunosuppression. We review clinical features and treatment protocols of COVID-19 patients with immunosuppression after treatment with human convalescent plasma. We also discuss the time course and clinical features of recovery. The available data from case reports and case series provide evidence suggesting a mortality benefit and rapid clinical improvement in patients with several forms of immunosuppression following COVID-19 convalescent plasma transfusion. The utility of convalescent plasma or other forms of antibody therapy in immune-deficient and immune-suppressed patients with COVID-19 warrants further investigation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".