Evaluation of the ID NOW Among Symptomatic Individuals During the Omicron Wave
Bibliographic record
Abstract
ABSTRACT BACKGROUND Point of Care SARS-CoV-2 devices, such as the Abbott ID NOW have great potential, to help combat the COVID-19 pandemic. Starting in December, 2020, the ID NOW was implemented throughout the province of Alberta, Canada (population 4.4 million) in various settings. We aimed to assess the ID NOW performance during the BA.1 Omicron wave and compare it to previous waves. METHODS The ID NOW was assessed in two distinct locations among symptomatic individuals: acute care (emergency room, urgent care, and hospitalized patients) and community assessment centres (AC) during the period January 5 – 18, 2022. Starting January 5, Omicron represented >95% of variants detected in our population. For every individual tested, two swabs were collected: one for ID NOW testing and the other for either reverse-transcriptase polymerase chain reaction (RT-PCR) confirmation of negative ID NOW results or for variant testing of positive ID NOW results. RESULTS A total of 3,041 paired samples were analyzed (1,139 RT-PCR positive). 1,873 samples were from 42 COVID-19 AC and 1,168 from 69 rural hospitals. ID NOW sensitivity for symptomatic individuals presenting to community AC and patients in hospital was 96.0% [95% confidence interval (CI) 94.5-97.3%, n=830 RT-PCR positive], and 91.6% (95% CI 87.9-94.4%, n=309 RT-PCR positive), respectively. SARS-CoV 2 positivity rate was very high for both populations (44.3% at AC, 26.5% in hospital). CONCLUSIONS Sensitivity of ID NOW SARS-CoV-2, compared to RT-PCR, is very high during the BA.1 Omicron wave, and is significantly higher when compared to previous SARS-CoV-2 variant waves.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".