Comparative analysis of triplex nucleic acid test assays in United States blood donors
Bibliographic record
Abstract
BACKGROUND: This study assessed the clinical sensitivity of three fully automated, human immunodeficiency virus (HIV), hepatitis C virus (HCV), and hepatitis B virus (HBV) triplex nucleic acid test (NAT) assays by individual donation (ID-NAT) and at operational minipool (MP-NAT) sizes used worldwide. STUDY DESIGN AND METHODS: MPX, Ultrio, and Ultrio Plus were used to test 2222 pedigreed, marker-positive samples with varying viral loads, each from a unique US blood donor. NAT-positive, seronegative yield samples (16 HBV, 156 HCV, and 23 HIV) were tested in replicates of three; undiluted; and in 1:6, 1:8, and 1:16 dilutions (MP6, MP8, and MP16), simulating various MP sizes. Seropositive samples (1276 HBV, 488 HCV, and 263 HIV) were tested by ID-NAT in singlet. RESULTS: MPX-MP6 and Ultrio Plus-MP16 had equivalent HCV sensitivity. Although Ultrio Plus-MP16 for HIV trended toward lesser sensitivity, this was not corroborated in a large substudy of low-viral-load samples in which Ultrio Plus-MP8/MP16 showed 100% reactivity. MPX-ID and Ultrio Plus-ID HBV clinical sensitivity were identical, but MPX-MP6 was significantly more sensitive than Ultrio Plus-MP16; the differential yield projected to one HBV NAT yield per 4.72 million US donations. Ultrio Plus HBV sensitivity did not increase at MP8 versus MP16. Ultrio Plus versus Ultrio sensitivity was significantly increased in HBV-infected donors with early acute, late acute or chronic, and occult infections. No difference in sensitivity was noted for any virus for MPX-MP6 versus Ultrio Plus-ID. CONCLUSIONS: Our data support US donation screening with MPX-MP6 or Ultrio Plus-MP16 since the HBV DNA detection of Ultrio Plus was significantly enhanced (vs. Ultrio) without compromising HIV or HCV RNA detection.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".