Effectiveness and feasibility of convalescent blood transfusion to reduce COVID-19 fatality ratio
Bibliographic record
Abstract
Background: As of December 2020, COVID-19 has spread all over the world with more than 81 million cases and more than 1.8 million deaths. The rapidly increasing number of patients mandates the consideration of potential treatments for patients under severe and critical conditions. Convalescent plasma (CP) treatment refers to the approach of infusing patients with plasma from recently recovered patients. CP appears to be a possible therapeutic option to manage patients suffering from severe or even lethal infectious disorders, in which ‘traditional therapies’ have failed to obtain any result. Methods: In the present study, we develop a mathematical model on the treatment-donation-stockpile dynamics for an optimal implementation of CP therapy to examine potential benefits and complications in the logistic realization of this therapy in a large-scale population. We parametrize the model with COVID-19 epidemics in Italy, and conduct scenario analyses to estimate outcomes of population-wide CP therapy and to examine the maximum number of CP donation processions per day. Results: Under the assumption that the efficacy of CP is 90%, we show that by the end of year 2020, initiating the population-wide CP therapy from April 2020 can save as many as 19 215 lives (ranging from 5000 to 28 000 depending on donor availability), while the demand for apheresis use is manageable in all scenarios: the maximum daily demand is 156 (ranging from 27 to 519 depending on donor availability) for the first outbreak wave and 1434 (ranging from 224 to 4817 depending on donor availability) for the second wave. Given that Italy has 61 centres with apheresis this maximum demand level corresponds to a daily average of 2.5 and 23.5 processions of CP donation being performed by each centre with respect to each outbreak wave. Conclusions: Our analyses show that population-wide CP therapy can contribute to curbing COVID-19-related deaths, and the logistic implementation is feasible for developed countries. The reduction of deaths can be very significant if the CP therapy is started earlier in the outbreak, and remains significant even if it is implemented during the outbreak peak time.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".