Development of a Rift Valley fever real-time RT-PCR assay that can detect all three genome segments
Bibliographic record
Abstract
Outbreaks of Rift Valley fever in Kenya, Madagascar, Mauritania, and South Africa had devastating effects on livestock and human health. In addition, this disease is a food security issue for endemic countries. There is growing concern for the potential introduction of RVF into non-endemic countries. A number of single-gene target amplification assays have been developed for the rapid detection of RVF viral RNA. This paper describes the development of an improved amplification assay that includes two confirmatory target RNA segments (L and M) and a third target gene, NSs, which is deleted in the Clone 13 commercial vaccine and other candidate vaccines. The assay also contains an exogenous RNA control added during the PCR setup for detection of amplification inhibitors. The assay was evaluated initially with samples from experimentally infected animals, after which clinical veterinary and human samples from endemic countries were tested for further evaluation. The assay has a sensitivity range of 66.7-100% and a specificity of 92.0-100% depending on the comparison. The assay has an overall sensitivity of 92.5%, specificity of 95% and a positive predictive value of 98.7%. The single-tube assay provides confirmation of the presence of RVFV RNA for improved confidence in diagnostic results and a "differentiate infected from vaccinated animals" (DIVA)--compatible marker for RVFV NSs--deleted vaccines, which is useful for RVF endemic countries, but especially important in non-endemic countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".