Comparative evaluation of three FDA-approved HIV Ag/Ab combination tests using a genetically diverse HIV panel and diagnostic specimens
Bibliographic record
Abstract
BACKGROUND: HIV Ag/Ab combination assays are recommended by CDC for routine screening and several HIV Ag/Ab combination tests are now FDA-approved. Maintaining high specificity and consistent sensitivity across diverse HIV strains is critical for these assays to accurately detect HIV infection and expedite delivery of patient results. OBJECTIVES: To evaluate performance of three FDA-approved HIV tests: ARCHITECT HIV Combo (Abbott), ADVIA Centaur HIV Combo (Siemens) and BioPlex HIV Ag-Ab (Bio-Rad). STUDY DESIGN: Sensitivity and specificity were evaluated using an extensive panel of 28 HIV infected human specimens and 17 cultured virus isolates representing multiple genotypes, 6 seroconversion panels, 4 human samples with acute infection, WHO p24 standard and 4020 clinical specimens. RESULTS: The p24 limit of detection (LOD) for the WHO standard was 0.19IU/ml, 0.70IU/ml, and 1.77IU/ml in BioPlex, ARCHITECT, and Centaur respectively. The distribution of LODs across 15 HIV-1 isolates was substantially narrower in ARCHITECT (5-33pg/ml) than in BioPlex (11-198pg/ml) and Centaur (6-384pg/ml). All assays detected antibodies to the majority of HIV-1 and HIV-2 variants. However, reduced sensitivity was observed for Centaur in detection of antibodies to HIV-1 group M (CRF02_AG), O and N variants. BioPlex and ARCHITECT showed better seroconversion sensitivity than Centaur, detecting one bleed (3-7 days) earlier in 4 (BioPlex) and 3 (ARCHITECT) of 6 seroconversion panels. ARCHITECT demonstrated the highest specificity (99.90-100%) compared to BioPlex (99.80%) and Centaur (99.42%). CONCLUSIONS: The overall performance of ARCHITECT and BioPlex was superior to Centaur, especially for detection of acute HIV infection.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".