International review of blood donation screening for anti‐HBc and occult hepatitis B virus infection
Bibliographic record
Abstract
BACKGROUND: Hepatitis B core antibody (anti-HBc) screening has been implemented in many blood establishments to help prevent transmission of hepatitis B virus (HBV), including from donors with occult HBV infection (OBI). We review HBV screening algorithms across blood establishments globally and their potential effectiveness in reducing transmission risk. MATERIALS AND METHODS: A questionnaire on HBV screening and follow-up strategies was distributed to members of the International Society of Blood Transfusion working party on transfusion-transmitted infectious diseases. Screening data from 2022 were assimilated and analyzed. RESULTS: A total of 30 unique responses were received from 25 countries. Sixteen respondents screened all donations for anti-HBc, with 14 also screening all donations for HBV DNA. Anti-HBc prevalence was 0.42% in all blood donors and 1.19% in new donors in low-endemic countries; however, only 44% of respondents performed additional anti-HBc testing to exclude false reactivity. 0.68% of anti-HBc positive, HBsAg-negative donors had detectable HBV DNA. Ten respondents did universal HBV DNA screening without anti-HBc, whereas four respondents did not screen for either. Deferral strategies for anti-HBc positive donors were highly variable. One transfusion-transmission from an anti-HBc negative donor was reported. DISCUSSION: Anti-HBc screening identifies donors with OBI but also results in the unnecessary deferral of a significant number of donors with resolved HBV infection and donors with false-reactive anti-HBc results. Whilst confirmation of anti-HBc results could be improved to reduce donor deferral, transmission risks associated with anti-HBc negative OBI donors must be considered. In high-endemic areas, highly sensitive HBV DNA testing is required to identify infectious donors.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".