Systematic Review of Diagnostic Accuracy of DiaSorin Liaison SARS-CoV-2 Antigen Immunoassay.
Bibliographic record
Abstract
Background: Quantification of SARS-CoV-2 antigens by means of rapid, high-throughput and fully-automated techniques has been proposed as a feasible alternative to overcome the current shortage of resources for routine molecular diagnostics. To this end, we provide here a systematic review of diagnostic accuracy of DiaSorin Liaison SARS-CoV-2 antigen immunoassay. Methods: An electronic search was conduced in Medline and Scopus, with no language or date restrictions (up to January 20, 2022), for identifying all published studies articles in which the diagnostic performance of the DiaSorin Liaison SARS-CoV-2 antigen immunoassay was compared with molecular diagnostic techniques. Results: The electronic search identified a final number of 11 studies, totalling 4449 oro- and naso-pharyngeal specimens. The pooled diagnostic sensitivity, specificity and area under the curve (AUC) of the Liaison SARS-CoV-2 antigen immunoassay in all samples were 0.51 (95%CI, 0.49-0.54), 1.00 (95%CI, 1.00-1.00) and 0.994 (95%CI, 0.990-0.998), respectively, whilst the overall concordance with molecular diagnostics was 82.1%. The pooled diagnostic sensitivity, specificity and AUC of the Liaison SARS-CoV-2 antigen immunoassay in specimens with high viral load (i.e., cycle threshold values <25-30) were 0.79 (95%CI, 0.75-0.82), 1.00 (95%CI, 0.99-1.00) and 0.911 (95%CI, 0.879-0.943), respectively, whilst the overall concordance with molecular diagnostics in such samples increased to 94.2%. Conclusion: The results of this systematic literature review suggest that there is sufficient accuracy of the DiaSorin Liaison SARS-CoV-2 antigen immunoassay in samples with high viral loads that would enable its reliable usage for identifying superspreaders, who are responsible for the vast majority of transmission events.
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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 | 0.003 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".