Using a Field Quantitative Real-Time PCR Test To Rapidly Identify Highly Viremic Rift Valley Fever Cases
Bibliographic record
Abstract
Approximately 8% of Rift Valley fever (RVF) cases develop severe disease, leading to hemorrhage, hepatitis, and/or encephalitis and resulting in up to 50% of deaths. A major obstacle in the management of RVF and other viral hemorrhagic fever cases in outbreaks that occur in rural settings is the inability to rapidly identify such cases, with poor prognosis early enough to allow for more-aggressive therapies. During an RVF outbreak in Kenya in 2006 to 2007, we evaluated whether quantitative real-time reverse transcription-PCR (qRT-PCR) could be used in the field to rapidly identify viremic RVF cases with risk of death. In 52 of 430 RVF cases analyzed by qRT-PCR and virus culture, 18 died (case fatality rate [CFR] = 34.6%). Levels of viremia in fatal cases were significantly higher than those in nonfatal cases (mean of 10(5.2) versus 10(2.9) per ml; P < 0.005). A negative correlation between the levels of infectious virus particles and the qRT-PCR crossover threshold (C(T)) values allowed the use of qRT-PCR to assess prognosis. The CFR was 50.0% among cases with C(T) values of <27.0 (corresponding to 2.1 x 10(4) viral RNA particles/ml of serum) and 4.5% among cases with C(T) values of >or=27.0. This cutoff yielded 93.8% sensitivity and a 95.5% negative predictive value; the specificity and positive predictive value were 58% and 50%, respectively. This study shows a correlation between high viremia and fatality and indicates that qRT-PCR testing can identify nearly all fatal RVF cases.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".