Evaluation of the BioMérieux VIDAS® HIV Duo Quick and Anti-HCV Assays for Dried Blood Spot Based Serosurveillance
Bibliographic record
Abstract
Abstract Serosurveillance is central to monitoring our progress towards HIV and HCV elimination targets proposed for 2030. However, serosurveillance systems are ineffective without reliable serological assays for the detection of HIV and HCV antibodies. Assays should also be compatible with dried blood spot (DBS) samples to facilitate biological sample collection. The VIDAS® HIV Duo Quick and Anti-HCV assays are sold as reagents strips and processed by the automated VIDAS® benchtop immunoanalyser. While both assays have shown excellent performance in serum and plasma, performance data in DBS samples is lacking. In our study, we evaluate the performance of the VIDAS® HIV Duo Quick and Anti-HCV assays in DBS (n = 725) collected during a cross-sectional serosurvey (the Transitions study). The VIDAS® HIV Duo quick had a sensitivity and specificity of 94.5% (95% CI: 85.1%, 98.5%) and 95.7% (95% CI: 93.9%, 97.0%) respectively. Likewise, the VIDAS® Anti-HCV had a sensitivity and specificity of 95.6% (95% CI: 91.6%, 97.8%) and 95.6% (95% CI: 93.5%, 97.0%) respectively. These assays are unlikely to be helpful in low-prevalence settings due to sub-optimal performance, but their performance could likely be improved by optimizing DBS elution protocols which was, unfortunately, not possible during our study.
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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 | 0.013 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".