The PRONTO study: Clinical performance of ID NOW in individuals with compatible SARS-CoV-2 symptoms in walk-in centres—accelerated turnaround time for contact tracing
Bibliographic record
Abstract
Background: (IDN) COVID-19 diagnostic assay used at point of care and its impact on turnaround time for divulgation of test results. Methods: Prospective study conducted from December 2020 to February 2021 in acute symptomatic participants presenting in three walk-in centres in the province of Québec. Results: Valid paired samples were obtained from 2,372 participants. A positive result on either the IDN or the standard-of-care nucleic acid amplification test (SOC-NAAT) was obtained in 423 participants (prevalence of 17.8%). Overall sensitivity of IDN and SOC-NAAT were 96.4% (95% CI: 94.2-98.0%) and 99.1% (95% CI: 97.6-99.8), respectively; negative predictive values were 99.2% (95% CI: 98.7-99.6%) and 99.8% (95% CI: 99.5-100%), respectively. Turnaround time for positive results was significantly faster on IDN. Conclusion: In our experience, IDN use in symptomatic individuals in walk-in centres is a reliable sensitive alternative to SOC-NAAT without the need for subsequent confirmation of negative results. Such deployment can accelerate contact tracing, reduce the burden on laboratories and increase access to testing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".