Risk of transmission of severe acute respiratory syndrome coronavirus 2 by transfusion: A literature review
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel human coronavirus responsible for coronavirus disease 2019 (COVID-19). The emergence of this virus in Wuhan, China, at the end of 2019 and its worldwide spread to reach the pandemic stage has raised concerns about the possible risk that it might be transmissible by transfusion. This theoretical risk is further supported by reports of the detection of viral RNA in the blood of some infected individuals. To further address this risk, a thorough PubMed literature search was performed to systematically identify studies reporting data on the detection of SARS-CoV-2 RNA in blood or its components. Complementary searches were done to identify articles reporting data on the in vitro infectivity of blood components. At least 23 articles presenting data on the detection of SARS-CoV-2 RNA in blood, plasma, or serum were identified. Of these, three studies reported on blood donors with COVID-19 infection identified after donation, and no cases of transfusion transmission were identified. A few studies mentioned results of in vitro infectivity assays of blood components in permissive cell lines, none of which were able to detect infectious virus in blood or its components. Complementary searches have identified reports demonstrating that the correlation between the presence of viral RNA in a biologic sample and infectivity requires a minimal RNA load, which is rarely, if ever, observed in blood components. Overall, the available evidence suggests that the risk of transmission of SARS-CoV-2 by transfusion remains theoretical.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".