Prevention of transfusion-transmitted hepatitis
Bibliographic record
Abstract
The most significant and cost-effective advances in reducing the risk of post-transfusion hepatitis have resulted from the selection of safer blood-donor populations (acceptance of volunteer rather than paid donors, banning of blood collections from prisoners) and the screening of blood for HBsAg (introduced in the early 1970s) 1 Alter HJ Holland PV Purcell RH et al. Post transfusion hepatitis after exclusion of commercial and hepatitis-B antigen positive donors. Ann Intern Med. 1972; 77: 691-699 Crossref PubMed Scopus (273) Google Scholar and hepatitis C antibody (introduced in the early 1990s). 1 Alter HJ Holland PV Purcell RH et al. Post transfusion hepatitis after exclusion of commercial and hepatitis-B antigen positive donors. Ann Intern Med. 1972; 77: 691-699 Crossref PubMed Scopus (273) Google Scholar , 2 Donahue JG Munoz A Ness PM et al. The declining risk of post-transfusion hepatitis C virus infection. N Engl J Med. 1992; 327: 369-373 Crossref PubMed Scopus (659) Google Scholar There is general agreement that, in developed countries, the blood supply is the safest that it has ever been. Nevertheless, strategies aimed at achieving the theoretical goal of zero risk continue to be assessed and implemented. Most of these strategies involve the application of the newer laboratory assays that can narrow the window period, the interval during which a donor unit is infectious but not yet detectable by current assays for blood-donor screening. According to a prospective study of donor-seroconversion rates in the USA, for hepatitis C (HCV) the number of “window-period units” released for transfusion was 1 in 103 000 of all units issued (95% CI 28 000–288 000), and for hepatitis B (HBV) it was 1 in 63 000 (31 000–147 000). 3 Schreiber GB Busch MP Kleinman SH Korelitz JJ The risk of transfusion-transmitted viral infections. N Engl J Med. 1996; 334: 1685-1690 Crossref PubMed Scopus (1612) Google Scholar
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".