Quantitative Measurement of Anti-SARS-CoV-2 Antibodies: Analytical and Clinical Evaluation
Bibliographic record
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). Molecular-based testing is used to diagnose COVID-19, and serologic testing of antibodies specific to SARS-CoV-2 is used to detect past infection. While most serologic assays are qualitative, a quantitative serologic assay was recently developed that measures antibodies against the S protein, the target of vaccines. Quantitative antibody determination may help determine antibody titer and facilitate longitudinal monitoring of the antibody response, including antibody response to vaccines. We evaluated the quantitative Roche Elecsys anti-SARS-CoV-2 S assay. Specimens from 167 PCR-positive patients and 103 control specimens were analyzed using the Elecsys anti-SARS-CoV-2 S assay on the cobas e411 (Roche Diagnostics). Analytical evaluation included assessing linearity, imprecision, and analytical sensitivity. Clinical evaluation included assessing clinical sensitivity, specificity, cross-reactivity, positive predictive value (PPV), negative predictive value (NPV), and serial sampling from the same patient. The Elecsys anti-SARS-CoV-2 S assay exhibited its highest sensitivity (84.0%) at 15 to 30 days post-PCR positivity and exhibited no cross-reactivity, a specificity and PPV of 100%, and an NPV between 98.3% and 99.8% at ≥14 days post-PCR positivity, depending on the seroprevalence estimate. Imprecision was <2% at 9.06 U/ml across 6 days, the negative quality control (QC) was consistently negative (<0.40 U/ml), the manufacturer's claimed limit of quantitation of 0.40 U/ml was verified, and linearity across the analytical measuring range was observed, except at the low end (<20 U/ml). Lastly, antibody response showed high interindividual variation in level and time of peak antibody titer and trends over time.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".