Analytical and clinical evaluation of four anti-SARS-CoV-2 serologic (IgM, IgG, and total) immunoassays
Bibliographic record
Abstract
Abstract Introduction Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is diagnosed by molecular-based detection of SARS-CoV-2 RNA. Serologic testing detects antibodies specific to SARS-CoV-2 and IgM specifically may serve as an adjunct test to PCR early in disease. We evaluated the Abbott anti-SARS-CoV-2 IgM and IgG assays along with DiaSorin anti-SARS-CoV-2 IgG and Roche anti-SARS-CoV-2 Total. Methods Specimens from 175 PCR-positive patients and 107 control specimens were analyzed using Abbott IgM and IgG, DiaSorin IgG, and Roche Total (IgA, IgG, IgM) assays. Sensitivity, specificity, cross-reactivity, concordance between assays, trends over time, positive predictive value (PPV), and negative predictive value (NPV) were determined. Results Abbott IgM sensitivity was 63.6% at 0 days post-PCR positivity, 76.5% at 1-5d, 76.3% at 6-14d, 85.2% at 15-30d, and 63.6% at >30d. All assays exhibited highest sensitivity 15-30d post-PCR positivity (83.3-85.2%). Combining Abbott IgM and IgG improved sensitivity by 22.7% compared to IgG alone when tested 0d post-PCR positivity. All assays had a specificity of 100% and only Abbott IgG exhibited cross-reactivity (anti-dsDNA). Cohen’s kappa varied between 0.86-0.93. Time to seroconversion from PCR positivity was lowest for Abbott IgM and highest for Abbott IgG. NPV was highest for Abbott IgM <14 days post-PCR positivity and Abbott IgG ≥14 days. Conclusion The Abbott IgM assay exhibited the earliest response and greatest signal in most patients evaluated for serial sampling and had the highest NPV <14 days post-PCR positivity, suggesting its potential utility as an adjunct test to PCR early in disease course.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".