Evaluation of Aggregate Oral Fluids for African Swine Fever Real–Time PCR Diagnostics Using Samples Collected on Romanian Farms with an Active Outbreak
Bibliographic record
Abstract
African swine fever (ASF), caused by African swine fever virus (ASFv), is a highly contagious disease of domestic and wild pigs with a mortality rate that can reach 100%. Continuous spread of the virus into ASF-free regions, including the Caribbean island of Hispaniola in 2021, is causing socioeconomic burdens and presents a threat to food security. Pork-producing countries, including the United States and Canada, are urgently looking for efficient tools for early detection to reduce spread of the virus in the event of an outbreak. Previous experimental infection studies evaluated the utility of aggregate porcine oral fluids (OFs) as a sample type with a highly sensitive ASFv real-time PCR for individual blood and tissue-based diagnosis. In this study, real-time PCR with porcine OFs was further evaluated to better understand diagnostic performance using samples from three Romanian farms with an ongoing ASF outbreak. In this limited dataset using a Bayesian latent class model, no statistical difference in diagnostic sensitivity was found between the real-time PCR using aggregate OFs and the process of determining pen disease status by testing individual blood samples collected from a subset of pigs from the same pen. Known negative aggregate OF samples from pigs in the United States had no occurrences of false positives, suggesting reliable diagnostic specificity of the sample matrix used for this study. Until results are produced from further studies with sufficient sample size, aggregate OF testing using real-time PCR could cautiously be used as a supplementary sample type for ASF diagnosis alongside currently approved sample types, including blood and lymphoid tissues.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".