Evaluation of Ortho VITROS and Roche Elecsys S and NC Immunoassays for SARS‑CoV‑2 Serosurveillance Applications
Bibliographic record
Abstract
Abstract SARS-CoV-2 seroprevalence studies are instrumental in monitoring epidemic activity and require well-characterized, high-throughput assays, and appropriate testing algorithms. The U.S. Nationwide Blood Donor Seroprevalence Study performed monthly cross-sectional serological testing from July 2020 to December 2021, implementing evolving testing algorithms in response to changes in pandemic activity. With high vaccine uptake, anti-Spike (S) reactivity rates reached >80% by May 2021, and the study pivoted from reflex Roche anti-nucleocapsid (NC) testing of Ortho S-reactive specimens to parallel Ortho S/NC testing. We evaluated the performance of the Ortho NC assay as a replacement for the Roche NC assay and compared performance of parallel S/NC testing on both platforms. Qualitative and quantitative agreement of Ortho NC with Roche NC assays was evaluated on pre-selected S/NC concordant and discordant specimens. All 200 Ortho S+/Roche NC+ specimens were reactive on the Ortho NC assay; 36% of 383 Ortho S+/Roche NC- specimens collected prior to vaccine availability and 52% of 101 Ortho S-/Roche NC+ specimens were reactive on the Ortho NC assay. Performance of parallel S/NC testing using Ortho and Roche platforms was evaluated on 200 specimens collected in 2019 and 3,903 study specimens collected in 2021. All 200 pre-COVID 2019 specimens tested negative on the four assays. Agreement of S and NC reactivity on specimens was 96.4% (3,769/3,903); most discordant results had reactivity close to the cutoffs on the alternate assays. These findings, and higher efficiency and throughput, support use of parallel S/NC testing on either Roche or Ortho platforms for large serosurveillance studies.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".