Evaluation of the ID NOW among symptomatic individuals during the Omicron wave
Bibliographic record
Abstract
Introduction. Starting in December, 2020, the ID NOW was implemented throughout the province of Alberta, Canada (population 4.4 million) in various settings. Gap statement. ID NOW’s test performance with SARS-CoV-2 Omicron variant BA.1 is unknown. Aim. To assess the ID NOW performance among symptomatic individuals during the BA.1 Omicron wave and compare it to previous SARS-CoV-2 variant waves. Methodology. The ID NOW was assessed in two locations among symptomatic individuals: rural hospitals and community assessment centres (AC) during the period 5–18 January 2022. Starting 5 January, 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 3041 paired samples were analysed (1139 RT-PCR positive). From this, 1873 samples were from 42 COVID-19 AC and 1168 from 69 rural hospitals. ID NOW sensitivity for symptomatic individuals presenting to community AC and rural hospitals 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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".