Evaluation of the performance of multiple immunoassay diagnostic platforms on the National Microbiology Laboratory SARS-CoV-2 National Serology Panel
Bibliographic record
Abstract
BACKGROUND: Serological assays designed to detect SARS-CoV-2 antibodies are being used in serological surveys and other specialized applications. As a result, and to ensure that the outcomes of serological testing meet high quality standards, evaluations are required to assess the performance of these assays and the proficiency of laboratories performing them. METHODS: A panel of 60 plasma/serum samples from blood donors who had reverse transcriptase–polymerase chain reaction (RT-PCR) confirmed SARS-CoV-2 infections and 21 SARS-CoV-2 negative samples were secured and distributed to interested laboratories within Canada ( n = 30) and the United States ( n = 1). Participating laboratories were asked to provide details on the diagnostic assays used, the platforms the assays were performed on, and the results obtained for each panel sample. Laboratories were blinded with respect to the expected outcomes. RESULTS: The performance of the different assays evaluated was excellent, with the high-throughput platforms of Roche, Ortho, and Siemens demonstrating 100% sensitivity. Most other high-throughput platforms had sensitivities of >93%, with the exception of the IgG assay using the Abbott ARCHITECT which had an average sensitivity of only 87%. The majority of the high-throughput platforms also demonstrated very good specificities (>97%). CONCLUSION: This proficiency study demonstrates that most of the SARS-CoV-2 serological assays utilized by provincial public health or hospital laboratories in Canada have acceptable sensitivity and excellent specificity.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".