Impact of a policy to permit the return of donors repeat‐reactive to the Abbott PRISM antibody to hepatitis B core antigen assay
Bibliographic record
Abstract
BACKGROUND: The expected donor loss from recent implementation of antibody to hepatitis B core antigen (anti-HBc) testing in Canada was uncertain but potentially significant based on US experience. To reduce donor loss from false-reactive tests, repeat-reactive donors without other evidence of infection were eligible to return. The aim was to evaluate the impact of anti-HBc testing on donor loss and to evaluate the effectiveness of this policy. STUDY DESIGN AND METHODS: For each donor in the first year of implementation (April 9, 2005-April 8, 2006) repeat-reactive for the presence of anti-HBc only but eligible to return (screening test for hepatitis B surface antigen-negative, plus not reactive to antibody to hepatitis B surface antigen [anti-HBs] and hepatitis B virus [HBV] DNA supplemental tests), 10 matched donors not reactive to the anti-HBc assay were selected. Return rates over 2 years were compared using conditional logistic regression. Testing outcomes were tabulated. RESULTS: Over the first year of testing, 412,236 donors (951,423 donations) were tested for anti-HBc, and 4,489 donors were repeat-reactive (1.3% of first-time donors, 1.0% of repeat donors). Of these 85.6 percent were also reactive for the presence of anti-HBs and/or HBV DNA supplemental tests leaving less than 15 percent eligible to return, of whom 73 percent returned (vs. 90% of controls, p < 0.001). Of the 300 anti-HBc repeat-reactive returning donors, 74 percent were anti-HBc repeat-reactive again (thus permanently deferred), 19 percent were deferred for other reasons versus 14 percent of controls (p < 0.05), and 7 percent (21 donors) did not react and were eligible to continue donating. CONCLUSION: Most donors repeat-reactive for the presence of anti-HBc likely have past exposure to HBV. If eligible, most are willing to return, but likely to test anti-HBc repeat-reactive again.
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 | 0.032 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".