A four specimen-pooling scheme reliably detects SARS-CoV-2 and influenza viruses using the BioFire FilmArray Respiratory Panel 2.1
Bibliographic record
Abstract
The COVID-19 pandemic required increased testing capacity, enabling rapid case identification and effective contract tracing to reduce transmission of disease. The BioFire FilmArray is a fully automated nucleic acid amplification test system providing specificity and sensitivity associated with gold standard molecular methods. The FilmArray Respiratory Panel 2.1 targets 22 viral and bacterial pathogens, including SARS-CoV-2 and influenza virus. While each panel provides a robust output of information regarding pathogen detection, the specimen throughput is low. This study evaluates the FilmArray Respiratory Panel 2.1 using 33 pools of contrived nasal samples and 22 pools of clinical nasopharyngeal specimens to determine the feasibility of increasing testing capacity, while maintaining detection of both SARS-CoV-2 and influenza virus. We observed 100% detection and 90% positive agreement for SARS-CoV-2 and 98% detection and 95% positive agreement for influenza viruses with pools of contrived or clinical specimens, respectively. While discordant results were mainly attributed to loss in sensitivity, the sensitivity of the pooling assay was well within accepted limits of detection for a nucleic acid amplification test. Overall, this study provides evidence supporting the use of pooling patient specimens, one in four with the FilmArray Respiratory Panel 2.1 for the detection of SARS-CoV-2 and influenza virus.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".