Validation of a competitive ELISA and a virus neutralization test for the detection and confirmation of antibodies to <i>Senecavirus A</i> in swine sera
Bibliographic record
Abstract
Senecavirus A (SVA; family Picornaviridae) is a nonenveloped, single-stranded RNA virus associated with idiopathic vesicular disease (IVD) in swine. SVA was detected in pigs with IVD in Brazil, United States, Canada, and China in 2015, triggering the need to develop and/or validate serologic assays for SVA. Our objective was to fully validate a previously developed competitive enzyme-linked immunosorbent assay (cELISA) as a screening test for antibodies to SVA. Additional objectives included the development and validation of a virus neutralization test (VNT) as a confirmatory test for SVA antibody detection, and the comparison of the cELISA, VNT, and an existing immunofluorescent antibody test (IFAT) for the detection of SVA antibodies in serial bleeds from SVA outbreaks. The diagnostic specificity and sensitivity were 98.2% (97.2-98.9%) and 96.9% (94.5-98.4%) for the cELISA, and 99.6% (99.0-99.9%) and 98.2% (95.8-99.4%) for the VNT, respectively. There was strong agreement among cELISA, VNT, and IFAT when compared based on kappa coefficient. Based on these performance characteristics, these tests are considered suitable for serologic detection of SVA in pigs.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".