Specificity of SARS-CoV-2 antibody-detection assays against S and N protein among pre-COVID-19 sera from patients with protozoan and helminth parasitic infections
Bibliographic record
Abstract
ABSTRACT Background We aimed to assess the specificity of SARS-CoV-2 antibody detection assays among people with known tissue-borne parasitic infections. Methods We tested three SARS-CoV-2 antibody-detection assays (cPass SARS-CoV-2 Neutralization Antibody Detection Kit, Abbott SARS-CoV-2 IgG assay, and STANDARD Q COVID-19 IgM/IgG Combo Rapid Test) among 559 pre-COVID-19 sera. Results The specificity of assays was 95-98% overall. However, lower specificity was observed among sera from patients with protozoan infections of the reticuloendothelial system, such as human African trypanosomiasis (Abbott Architect; 88% [95%CI 75-95]), visceral leishmaniasis (SD RDT IgG; 80% [95%CI 30-99]), and from patients with recent malaria from a holoendemic area of Senegal (ranging from 91% for Abbott Architect and SD RDT IgM to 98-99% for cPass and SD RDT IgG). For specimens from patients with evidence of past or present helminth infection overall, test specificity estimates were all ≥ 96%. Sera collected from patients clinically suspected of parasitic infections that tested negative for these infections yielded a specificity of 98-100%. The majority (>85%) of false-positive results were positive by only one assay. Conclusions The specificity of SARS-CoV-2 serological assays among sera from patients with tissue-borne parasitic infections was below the threshold required for decisions about individual patient care. Specificity is markedly increased by the use of confirmatory testing with a second assay. Finally, the SD RDT IgG proved similarly specific to laboratory-based assays and provides an option in low-resource settings when detection of anti-SARS-CoV-2 IgG is indicated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".