Avoiding false positive SARS-CoV-2 rapid antigen test results with point-of-care molecular testing on residual test buffer
Bibliographic record
Abstract
Abstract Objectives Antigen-based rapid diagnostic tests (Ag-RDTs) have been widely used for the detection of SARS-CoV-2 during the Covid-19 pandemic. In settings of low disease prevalence, such as asymptomatic community testing, national guidelines recommend molecular confirmation of positive Ag-RDT results. This often requires patients to be recalled for repeat specimen recollection and subsequent testing in reference laboratories. This project assessed the use of a point-of-care molecular method for SARS-CoV-2 detection on-site at a volunteer-led asymptomatic community testing site, using the residual test buffer (RTB) from positive Ag-RDTs. Methods The Abbott COVID-19 ID NOW assay was performed on RTB from two Ag-RDTs: the Abbott Panbio COVID-19 Ag Rapid Test Device and the BTNX Rapid Response COVID-19 Antigen Rapid Test Device. All RTBs were tested using real-time RT-PCR at a reference laboratory using the ThermoFisher TaqPath COVID-19 Combo kit which was used to assign positive Ag-RDTs results as true or false positives. Analytical specificity of the ID NOW was assessed with a panel of various respiratory organisms. Results Of 419 positive Ag-RDTs from 5148 tests performed, ID NOW testing of the RTB was positive in 100% of the samples characterized as true positives by RT-PCR. No SARS-CoV-2 detections by ID NOW were observed from 10 specimens characterized as false positive Ag-RDTs, or from contrived specimens with various respiratory organisms. Conclusions The use of on-site molecular testing on RTB provides a suitable option for rapid confirmatory testing of positive Ag-RDTs, thereby obviating the need for specimen recollection for molecular testing at local reference laboratories.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".