Integration of nucleic acid amplification test results into hepatitis C virus supplemental serologic testing algorithms: implications for donor counseling and revision of existing algorithms
Bibliographic record
Abstract
BACKGROUND: The routine use of hepatitis C virus (HCV) nucleic acid amplification testing (NAT) donor screening assays has provided an opportunity for revision of the current HCV supplemental testing algorithm, which requires that recombinant immunoblot assay (RIBA) be performed on every HCV enzyme immunoassay (EIA)-repeat-reactive donation. The FDA has approved variance requests to use a new algorithm that eliminates the need to perform RIBA when HCV NAT results are reactive. Data are provided in support of this new algorithm. STUDY DESIGN AND METHODS: HCV EIA (including signal-to-cutoff optical density ratio), RIBA, and NAT data were compiled from 33.2 million donations screened over an approximately 4-year period by the American Red Cross and Blood Systems Laboratories. Further, donations having specific combinations of HCV EIA, RIBA, and minipool (MP) NAT results were evaluated, with more sensitive individual-donation (ID) NAT, to construct improved counseling messages for donors. RESULTS: Of 47,041 EIA-repeat-reactive donations, 49.3 percent were RIBA-positive, 17.1 percent RIBA-indeterminate, and 33.5 percent RIBA-negative. NAT-reactive rates were 79.2, 2.5, and 0.18 percent for RIBA-positive, -indeterminate, and -negative donations, respectively. The new algorithm classified an additional 1 percent of donations as HCV-infected while at the same time detecting all infections classified as HCV-infected under the current algorithm. An additional 2.4 percent of RIBA-positive, MP NAT-nonreactive donations were reactive when a frozen-thawed aliquot was retested by ID NAT. CONCLUSION: Integrating HCV NAT results with RIBA results for purposes of donor notification allows more appropriate counseling messages to be given to EIA-repeat-reactive donors. The new HCV supplemental algorithm is an acceptable alternative to the current algorithm because it provides equivalent or superior accuracy in formulating donor counseling messages and may also result in reduced costs and more timely notification of infected donors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".