Clinical performance of the BD Respiratory Viral Panel – SCV2 for BD MAX™ system with nasopharyngeal and anterior nasal specimens
Bibliographic record
Abstract
Background The detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relies heavily on highly sensitive and specific assays. While nasopharyngeal (NP) specimens are considered the gold standard, it is crucial that current assays also support the use of anterior nasal swab (NS) specimens, as they can be more suitable in situations where NP specimen collection is difficult or impractical. Methods Paired NP and NS specimens prospectively collected from symptomatic and asymptomatic participants were utilized to evaluate the BD Respiratory Viral Panel - SCV2 for BD MAX™ System (BD RVP SCV2) clinical performance against a composite comparator of three CE-marked assays: cobas® SARS-CoV-2, Aptima® SARS-CoV-2, and Lyra® SARS-CoV-2. Concordance between at least two assays established a positive or negative comparator result. Each specimen was divided into four aliquots, one for each assay. BD RVP SCV2 performance was assessed by comparing results from NP specimens (NP vs NP), NS specimens (NS vs NS), and NS results with paired NP comparator results (NS vs NP). Positive and negative percent agreements (PPA and NPA) were calculated for all comparisons. Results Combined symptomatic and asymptomatic specimens, when tested with the BD RVP SCV2 assay, met all set acceptance criteria, with PPAs of 98.7 % (NP vs NP), 98.4 % (NS vs NS), and 92.6 % (NS vs NP) while NPAs ranged from 97.7 % to 98.0 %. Conclusions These findings confirm that the BD Respiratory Viral Panel - SCV2 for BD MAX System performs well for detecting SARS-CoV-2 in NP and NS specimens from symptomatic and asymptomatic populations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".