Accuracy of rapid antigen testing for COVID-19 in shelter settings
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic disproportionally affected congregate living settings, including shelters. COVID-19 transmission can have more adverse outcomes in these settings due to the vulnerability of residents. Point of care rapid antigen testing (RAT) represents a strategy with potential benefits for COVID-19 detection in shelters, including rapid results, ease of use, cost-effectiveness, and early detection. Objectives Our primary objective was to assess the real-world test accuracy of RAT for COVID-19 using the Quidel Sofia 2 Flu + SARS Antigen fluorescent immunoassay (Sofia RAT) compared to polymerase chain reaction (PCR) testing among shelter residents in Ontario, Canada. Study Design A consecutive sample of 102 residents across six shelters who were symptomatic for, or exposed to COVID-19 were included. The RAT and PCR samples were taken on the same day for each participant. Results from the Sofia RAT were compared to PCR test results to determine test accuracy. Participant demographic data could not be collected due to workforce constraints. Results We reported our methods and findings using the QUality Assessment tool of Diagnostic Accuracy Studies (QUADAS-2) guidelines. Sofia 2 RAT specificity was 97.9% (95% CI: 92.7% to 99.7%) for COVID-19 compared to PCR. Due to a lack of true positive cases, sensitivity could not accurately be calculated (0.00% (95% CI: 0.00% to 52.2%)). Conclusion These data suggest that the Sofia RAT is a highly specific test for COVID-19.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".