Use of convalescent plasma in<scp>COVID</scp>‐19 patients with immunosuppression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it