Diagnostic performance of the fully automated Roche Elecsys SARS-CoV-2 antigen electrochemiluminescence immunoassay: a pooled analysis
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: Among the diagnostic tests that have recently become commercially available for diagnosing coronavirus disease 2019 (COVID-19), the fully-automated Roche Elecsys severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen electrochemiluminescence immunoassay (ECLIA) is one of the most widespread for its adaptability within a system of laboratory automation, rapidity and high-throughput. This article is aimed to provide the results of the first pooled analysis of its accuracy for diagnosing SARS-CoV-2 infections. CONTENT: We carried out an electronic search in Scopus and Medline, without language or date restrictions (i.e., up to January 18, 2022), to identify articles where the diagnostic performance of Roche Elecsys SARS-CoV-2 antigen ECLIA was compared with that of reference molecular diagnostic techniques. SUMMARY: Overall, 11 studies were identified, 10 of which (n=6,095 swabs) provided necessary data for inclusion in a pooled analysis. The pooled diagnostic sensitivity, specificity and area under the curve (AUC) in nasopharyngeal samples were 0.68 (95%CI, 0.66-0.70), 0.99 (95%CI, 0.99-0.99) and 0.958 (95%CI, 0.936-0.980), respectively. The cumulative observed agreement with reference molecular assays was 89.5% and the kappa statistic was 0.735 (95%CI, 0.716-0.754). The pooled diagnostic sensitivity in samples with high viral load (i.e., cycle threshold values <28-30) was 0.95 (95%CI, 0.92-0.97). OUTLOOK: The results of this pooled analysis confirm that the fully-automated Roche Elecsys SARS-CoV-2 antigen ECLIA has high diagnostic specificity and optimal diagnostic sensitivity for identifying nasopharyngeal samples with higher viral load, thus making it a reliable technique for mass screening and for supporting strategies based on shorten isolation and/or quarantine.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it