Fujirebio Lumipulse SARS-CoV-2 antigen immunoassay: pooled analysis of diagnostic accuracy
Bibliographic record
Abstract
We provide here a pooled analysis of accuracy of Fujirebio Lumipulse SARS-CoV-2 Antigen chemiluminescent immunoassay for diagnosing acute SARS-CoV-2 infections. An electronic search was conducted in Scopus and Medline with the keywords "Lumipulse" AND "antigen" AND "SARS-CoV-2" or "COVID-19", up to January 21, 2022, for identifying clinical investigations (minimum sample size ≥100) where diagnostic accuracy of Lumipulse G SARS-CoV-2 Ag was tested against reference molecular techniques. All studies which allowed to construct a 2 × 2 table were included in a pooled analysis. A final number of 21 studies, totalling 17,648 nasopharyngeal and 8538 saliva specimens, were finally included. The pooled diagnostic sensitivity and specificity in nasopharyngeal swabs were 0.80 (95%CI, 0.78-0.81) and 0.98 (95%CI, 0.97-0.98), respectively, whilst the area under the curve and agreement were 0.980 (95%CI, 0.973-0.986) and 94.9%, respectively. In the twelve studies which used the fixed 1.34 pg/mL currently recommended manufacturer's threshold, the diagnostic accuracy remained unvaried. In saliva samples, the pooled diagnostic sensitivity and specificity were 0.75 (95%CI, 0.71-0.75) and 1.00 (95%CI, 0.99-1.00), respectively, whilst the area under the curve and were 0.976 (95%CI, 0.969-0.984) and 98.4%, respectively. In the five studies which used the fixed 0.67 pg/mL currently recommended manufacturer's threshold, the diagnostic accuracy remained unvaried. In conclusion, Lumipulse G SARS-CoV-2 Ag assay demonstrates good diagnostic sensitivity and specificity, thus representing a valuable complementary and integrative option to molecular testing for SARS-CoV-2 in the current pandemic.
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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 | 0.084 | 0.173 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.054 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".