Hepatitis C infection: recent insights relevant to transfusion safety
Bibliographic record
Abstract
Hepatitis C virus (HCV) infects over 100 million persons worldwide, and is hence one of the most prevalent and clinically significant blood‐borne pathogens. The introduction of HCV antibody screening in the early 1990s led to detection of large numbers of infected donors and dramatic increases in blood safety. The seroprevalence of HCV in blood donors varies, ranging from rates as low as 0·01% in South Africa to ~0·5% in most developed countries, to > 5% in focal endemic areas such as Egypt. Studies of HCV in infected donors and recipients were instrumental in the discovery of HCV, and have yielded ongoing insights into HCV epidemiology, natural history and pathogenesis. Recent examples include detailed characterization of the dynamics of acute HCV infection based on studies of plasma donor panels, and important contributions to understanding viral and host factors influencing HCV transmission and disease outcome based on linked donor‐recipient cohort studies. The addition of nucleic acid testing (NAT) for HCV RNA to routine HCV antibody screening of donors in 1999 further reduced the risk of transfusion‐transmitted HCV. Mini‐pool (MP)‐NAT screening has led to detection of well over 500 donors in the viraemic, preseroconversion phase of primary HCV infection, as well as to discrimination of seropositive donors into those with resolved and chronic infections. Studies of donors with acute, chronic/persistent and resolved HCV infections have further contributed to our understanding of viral and host genetic and immunological determinants of spontaneous clearance of HCV and short‐ and long‐term disease outcomes. This review will highlight these past scientific contributions, and present the results of a recent survey of the yield of HCV MP‐NAT screening and estimated residual risks of MP‐NAT screened transfusions in different regions of the world. Finally, the review will discuss available evidence for the infectivity of very low‐level viraemia that may be missed by MP‐NAT, information that is pivotal to modelling the residual risk of MP‐NAT screened blood transfusions, and which will guide further strategies to reduce risk by further enhancing screening or implementation of pathogen‐inactivation technologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".