Cache Valley virus: A scoping review of the global evidence
Bibliographic record
Abstract
Cache Valley virus (CVV) is a mosquito-borne RNA virus detected throughout North America, Central America and parts of South America. A limited number of human case reports have described severe illness. CVV infection has been associated with outbreaks of congenital defects in small ruminants in Canada and the United States. A scoping review was conducted to identify, characterize and summarize research on CVV, and to identify research gaps. A structured search was conducted in eight electronic databases, with additional search verification and grey literature investigation. All captured studies were independently appraised by two reviewers for relevance and data characterization. The review captured 143 relevant studies investigating CVV epidemiology (n = 104), pathogenesis (n = 37), viral characteristics (n = 24), transmission (n = 14), diagnostic test performance (n = 8) and mitigation strategies (n = 2). Evidence of CVV infection was found in mosquito studies (n = 47), and serological evidence of exposure was demonstrated in animals (n = 41), as well as human (n = 20) studies. In sheep, five outbreaks of birth defects following asymptomatic dam CVV infection during the first 50 days of pregnancy were reported. Only six human cases of CVV-associated illness were captured, with case symptoms described as initially non-specific, progressing to more severe clinical signs (e.g., meningitis). No research was identified investigating treatment, societal knowledge and risk perception, economic burden or predictive models related to the impact of climate change on CVV. CVV circulates in mosquito and animal species across a large area of the Americas. Small ruminants are the only animals in which CVV-associated clinical disease has been extensively studied. It is likely that human cases are under-reported or misdiagnosed. Future research should focus on the impact of CVV infection in human and animal populations.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".