Evaluation of water gargle samples for SARS‐CoV‐2 detection using Abbott ID NOW COVID‐19 assay
Bibliographic record
Abstract
Abstract The Abbott ID NOW™ COVID‐19 assay has been shown as a reliable and sensitive alternative to reverse transcription‐polymerase chain reaction (RT‐PCR) testing from nasopharyngeal or nasal samples in symptomatic patients. Water gargle is an acceptable noninvasive alternative specimen for severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) detection by RT‐PCR. The objective of this study was to evaluate the performance of water gargle samples for the detection of SARS‐CoV‐2 using the ID NOW. Residual gargle samples were randomly selected among positive standard of care (SOC)‐nucleic acid amplification test (NAAT) samples. For testing on ID NOW, the manufacturer's instructions were followed, except for the specimen addition step: 500 µl of the gargle specimen was added to the blue sample receiver with a pipette and gently mixed. Among the 202 positive samples by SOC‐NAAT, 185 were positive by ID NOW (positive percent agreement [PPA]) = 91.6% (95% confidence interval [CI]: 86.9−95.0). For the 17 discordant samples, cycle threshold (Ct) values were all ≥31.0. The PPA was significantly lower among asymptomatic patients (84.4%; 95% CI: 73.2−92.3) versus symptomatic patients (95.2%; 95% CI: 89.8−98.2). The performance of the ID NOW for the detection of SARS‐CoV‐2 infection on gargle samples is excellent when Ct values are <31.0 and for patients that have COVID‐19 compatible symptoms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".