Evaluation of a Porcine Circovirus Type 2–Specific Antigen-Capture Enzyme-Linked Immunosorbent Assay for the Diagnosis of Postweaning Multisystemic Wasting Syndrome in Pigs: Comparison with Virus Isolation, Immunohistochemistry, and the Polymerase Chain Reaction
Bibliographic record
Abstract
Quantitative virus isolation, immunohistochemistry, polymerase chain reaction (PCR) assay, and a porcine circovirus 2 (PCV2)-specific antigen-capture enzyme-linked immunosorbent assay (ELISA) were used for differentiation between clinical and subclinical PCV2 infections of swine. Tissue samples from pigs experimentally infected with PCV2 and field cases of postweaning multisystemic wasting syndrome and PCV2-associated reproductive disorders were used in this evaluation. In initial studies on 6 PCV2 pools using 3 previously published PCR protocols for PCV2 detection, quantitative virus isolation, and antigen-capture ELISA, substantial differences in sensitivity were identified among these procedures. Examination of tissue samples from diseased and clinically normal pigs indicated that immunohistochemistry, quantitative virus isolation, and antigen-capture ELISA could be used to differentiate between clinical and subclinical PCV2 infections, but the PCR assay could not. Because subclinical infections of pigs with PCV2 are common, the use of nonquantitative PCR as a diagnostic tool for PCV2-related diseases should be discouraged and the PCV2-specific antigen-capture ELISA evaluated further.
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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 | 0.004 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".