Performance of BioFire Array or QuickVue Influenza A+B Test versus a validation qPCR assay for detection of influenza A during a volunteer A/California/2009/H1N1 challenge study
Bibliographic record
Abstract
Abstract Background: Influenza places a significant burden on global health and economics. Individual case management and public health efforts to mitigate the spread of influenza are both strongly impacted by our ability to accurately and efficiently detect influenza viruses in clinical samples. Therefore, it is important to understand the performance characteristics of available assays to detect influenza in a variety of settings. We provide the first report of relative performance between two products marketed to streamline detection of influenza virus in the context of a highly controlled volunteer influenza challenge study. Methods: Nasopharyngeal swab samples were collected during a controlled A/California/2009/H1N1 influenza challenge study and analyzed using for detection of virus shedding using a validated qRT-PCR (qPCR) assay, a sample-to-answer qRT-PCR device (BioMerieux BioFire FilmArray RP), and an immunoassay based rapid test kit (Quidel QuickVue Influenza A+B Test).Results: Relative to qPCR, the sensitivity and specificity of the BioFire assay was 72.1% (63.7%-79.5%, 95% Confidence Interval (CI)) and 93.5% (89.3%-96.4%, 95% CI) respectively. For the QuickVue rapid test the sensitivity was 8.5% (4.8%-13.7%, 95% CI) and specificity was 99.2% (95.6%-100%, 95% CI).Conclusion: Relative to qPCR, the BioFire assay had superior performance compared to rapid test in the context of a controlled influenza challenge 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".