La Crosse virus: a scoping review of the global evidence
Bibliographic record
Abstract
Urbanisation and climate change are altering the pattern of California serogroup viruses in North America. As La Crosse virus (LACV) is the most pathogenic of the California serogroup, it is important to identify changes in distribution, transmission and pathogenesis. A scoping review (ScR) was prioritised to summarise the global evidence on LACV. A comprehensive search strategy was used, identified references were screened for relevance and relevant articles were characterised. Each step was conducted by two independent reviewers using pre-tested forms. Analysis identified areas of research saturation and gaps. The ScR included 481 research articles that were mostly journal articles (78.2%) conducted in North America (90.9%) from 1969 to 2016. Most evidence focused on epidemiology (44.9%), virus characteristics (25.8%), transmission conditions (18.7%) and pathogenesis of LACV in hosts (18.3%). Fewer studies evaluated the accuracy of diagnostic tests (8.7%), the efficacy of treatments (3.5%), prevention and control strategies (3.1%), the economic burden of infection (0.6%) and social impact (0.2%) of LACV. None of the literature predicted the impact of climate change on LACV, nor were any cases reported in Canada. These findings are intended to guide research to close knowledge gaps and inform evidence-based decisions surrounding activities for the prevention and control of LACV.
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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 | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".