One swab, two tests: Validation of dual SARS-CoV-2 testing on the Abbott ID NOW™
Bibliographic record
Abstract
BACKGROUND: Point-of-care tests (POCT) are promising tools to detect SARS-CoV-2 in specific settings. Initial reports suggest the ID NOW™ COVID-19 assay (Abbott Diagnostics Inc, USA) is less sensitive than standard real-time reverse transcription polymerase chain reaction (rRT-PCR) assays. This has raised concern over false negatives in SARS-CoV-2 POCT. OBJECTIVES: We compared the performance of the ID NOW™ COVID-19 assay to our in-house rRT-PCR assay to assess whether dry swabs used in ID NOW™ testing could be stored in transport media and be re-tested by rRT-PCR for redundancy and to provide material for further investigation. METHODS: Paired respiratory swabs collected from patients at three acute care hospitals were used. One swab in transport media (McMaster Molecular Media (MMM)) was tested for SARS-CoV-2 by a laboratory-developed two-target rRT-PCR assay. The second was stored dry in a sterile container and tested by the ID NOW™ COVID-19 assay. Following ID NOW™ testing, dry swabs were stored in MMM for up to 48 h and re-tested by rRT-PCR. Serially diluted SARS-CoV-2 particles were used to assess the impact of heat inactivation and storage time. RESULTS: Respiratory swabs (n = 343) from 179 individuals were included. Using rRT-PCR results as the comparator, the ID NOW™ COVID-19 assay had positive (PPA) and negative (NPA) percent agreements of 87.0% (95% CI:0.74-0.94) and 99.7% (95% CI:0.98-0.99). Re-tested swabs placed in MMM following ID NOW testing had PPA and NPA of 88.8% (95% CI:0.76-0.95) and 99.7% (95% CI:0.98-0.99), respectively. CONCLUSIONS: Storing spent dry swabs in transport media for redundancy rRT-PCR testing is a potential approach to address possible false negatives with the ID NOW™ COVID-19 assay.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".