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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".